生产线平衡优化

The study on assembly line balancing theory and concept, focusing on the textile sector in Karachi, Pakistan, specifically the stitching department of a company, aimed to enhance efficiency through meticulous analysis and optimization. By employing time and motion analysis techniques, the research sought to identify and eliminate redundant activities, ensuring a seamless workflow with minimal idle time. The methodology employed involved a comprehensive observation of the entire production process, starting from stitching through to packing. This involved using a stopwatch to meticulously analyze each step of the process, thus gaining insight into the current operational dynamics of the assembly line. Line balancing was ensured by calculating Standard Allowed Minutes (SAM), assessing capacity, and determining the required manpower and machine resources. To further refine the efficiency of the stitching line, data collection was conducted using stopwatch techniques, to devise an improved layout. By reducing processing time and minimizing unproductive intervals, the study aimed to establish a foundation for enhanced productivity and streamlined work orders. Various tools were utilized to scrutinize the procedures involved in the production process, encompassing activities, operator proficiency, equipment efficiency, and material usage. This comprehensive approach aimed to optimize the workflow, making it more ergonomic and conducive to efficient operations while ensuring worker satisfaction. The study's analysis specifically targeted the stitching operations related to sheet sets, with a focus on minimizing the number of machines and achieving optimal line capacity. Significant reductions in machine requirements were achieved, with the number of machines for both sheet sets and fitted sheets being reduced substantially. Capacity calculations based on SAM and machine numbers demonstrated the achievement of production targets. Moreover, manpower requirements were reassessed and optimized, resulting in a reduction in the number of workers required for packing operations, thereby further enhancing efficiency.

This paper presents a solid methodology for improving the efficiency and productivity of assembly lines using Lean Manufacturing tools, in particular the Define, Measure, Analyze, Improve, and Control approach (DMAIC) and line balancing techniques, followed by a concrete application in a case study of a wiring industry assembly line. The first phase of the approach ensured a clear definition of the problem using the who, what, where, when, why, and how tool (5W1H) and a description of the manufacturing process. The measurement phase allowed the calculation of the Takt time (TT) and the timing of the cycle times of the 17 stations of the line with the use of data collected on the standardized work combination table (SWCT) documents. This facilitated the analysis phase by first establishing a Yamazumi chart showing the distribution of the load between the line’s stations and allowing the identification of bottleneck stations, and then analyzing the situation through the 5-Why tools and the Ishikawa diagram. Thanks to the innovation phase and the ideal balancing conditions developed in this paper, it was possible to balance the line’s stations using an action plan whose effectiveness was monitored during the control phase, improving efficiency from 78% to 95% with a saving in manpower by reducing the number of operators from 17 to 14.

The mixed work assignment technique was applied to the modular production concept to solve the apparel assembly line balancing problem. Specifically, an algorithm was implemented to generate workstations by first classifying tasks into modules through an analysis of the manufacturing process and assigning grouped tasks in the single task–multiple workers and multiple tasks–single worker assignment methods. Then, worker assignment was sequentially performed considering the skill level of the worker. A simulation of producing 100 men’s shirts was performed. Owing to line balancing, the total production time was considerably lower than that reported in previous studies. The enhanced performance was attributable to the fact that unlike previous studies that limited the apparel assembly line balancing problem to worker assignment, line balancing was performed through both task and worker assignment in this study. The results demonstrated that task assignment must be considered in the design stage in the apparel assembly line balancing problem.

The human–robot collaborative assembly line has been applied as an effective strategy in production to further improve the efficiency, adaptability, and flexibility. The assembly line balancing is an important part of assembly line design and optimisation. However, the current research often neglects the key factor of the number of stations, which lacks better guidance for the actual assembly line design and optimisation. To provide a better practical reference, this research first takes the number of stations, the makespan and the balance ratio as the optimisation objectives, and constructs a corresponding model. Then, an improved dual-population genetic algorithm is designed to solve the problem more efficiently. This algorithm optimises multiple operators based on the traditional genetic algorithm. The efficacy of the proposed algorithm is substantiated through numerical experimentation with three traditional heuristic algorithms. The experimental results show that the improved algorithm has superior performance in solving the human–robot collaborative assembly line balancing problem. On the one hand, the average improvement of fitness in different scale cases reaches 3.14% (up to 52.33%), and the distribution of solutions is more stable. On the other hand, the convergence speed is faster, the average reduction of convergence time is 11.50% (up to 88.24%).

Recently Indonesian textile and garment manufacturer has experienced a problem with shop floor production. The complexities in the manufacturing process led to many problems such as the inefficiency, and thus prevented the company from achieving its target. In fact, even though the company has established the efficiency target of 80%, the production floor cannot realize it. Thus, this research aims to increase the line efficiency to reach the company’s target. At the beginning of the analysis, the efficiency of assembly line was only 51,68%. Since, this value did not meet the company’s target and was not satisfying; the concept of ECRS was applied. The purpose of this research is to simplify the method to provide better effect and process flow. Before applying the method, the fishbone diagrams were used. The factors of man, method, machine and measurement were used to describe the root cause of the losses. Thus, after applying the concept of ECRS, the efficiency level increased to 81,54%, which had met the company’s target. The assembly line will run better and smoother with the smaller possibility of bottleneck if all of the workstations have a relatively balanced workload.

In the dynamic field of advanced manufacturing, integrating collaborative robots (cobots) into assembly lines has emerged as a transformative approach to improve efficiency and human job quality. This paper addresses the safety challenge of human–robot collaboration (HRC) in the context of an assembly line balancing problem (ALBP) aimed at minimising cycle time. A constraint programming (CP)-based model optimally assigns cobots to workstations, allocates assembly tasks between humans and cobots, and sequence them on a single-model line. The CP model includes a novel workstation zoning policy to eliminate the medium and high-risk parallel tasks in a collaborative workstation. Numerical examples illustrate the impact of the proposed policy. Comparing the optimal results in the presence and absence of zone constraints in HRC-ALBP reveals improved safety at the cost of a minor increase in cycle time. This contributes to bridging the gap between HRC safety and operational goal. With minor modifications, a second version of the model minimises the number of cobots used, providing further insight into the optimisation of cobot integration. By considering different objective functions, safety-oriented constraints, and managerial insights, this work contributes to the creation of a decision-support tool that aligns with the human-cantered principles of Industry 5.0.

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Energy consumption and cycle time are two contradictory optimization objectives for the robotic assembly line balancing problem (RALBP). Indeed, minimizing the cycle time leads to choosing the fastest robots, while minimizing the energy consumption leads to choosing the robots with the smallest powers. In the context of RALBP, cycle time minimization has been extensively studied while energy minimization has been much less considered. Studies dealing with simultaneous minimization of the two later are even scarcer. A bi‐objective RALBP considering simultaneous minimization of cycle time and energy consumption is studied in this paper. The energy consumption is calculated based on recent papers from the literature. It includes energy consumed during both operation time and idle time. In this paper, a pseudo‐polynomial case is solved thanks to an exact algorithm called split. This latter enumerates all Pareto‐optimal solutions corresponding to a given giant sequence of operations. Split is then used as a decoder in a metaheuristic operating in a reduced search space where giant sequences encode solutions. An experimental study is performed on instances taken from the literature to test the suggested encoding–decoding scheme. It shows that the suggested approach yields competitive results compared to the literature.

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Consideration is given to the robotic assembly line balancing problem (RALBP) under uncertain task (operation) times, a critical challenge encountered in automated manufacturing systems.. RALBP is a decision problem which seeks the optimal assignment of the assembly work as well as the most suitable robots to the workstations of the assembly line with respect to objectives related to the capacity of the line or/and its cost of operation. When multiple types of robots with different capabilities are being used, task times may vary depending on robot type and the nature of the task. Task variation is expected to be small for simple tasks but may be quite large for complex and failure sensitive operations. To deal with uncertainty in task variation we used fuzzy logic theory. First, we introduce formally the fuzzy RALBP and then we describe deeply the fuzzy representation of the task times. We address RALBP with respect to two optimization objectives namely, the production rate and workload smoothing. Since the problem is known to be NP-hard, we explore its heuristic solution through a new robust multi-objective genetic algorithm (MOGA) aiming to determine the Pareto optimal set. Simulation experiments assess MOGA’s efficiency in comparison to the famous NSGA-II and MOPSO algorithms, while also exploring the trade-off between the two conflicting objectives.

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As the manufacturing industry advances, efficient waste recycling has become a key focus in disassembly and assembly line problems. This work presents a disassembly and assembly line balancing problem (DALBP) that incorporates the switching time of the robot's operation direction. Unlike traditional disassembly lines balancing problem, the proposed model can enhance production efficiency from both lines. A single-object programming model is developed to optimize the switching time of robot operation. An improved Q-lambda (IQ(λ)) reinforcement learning algorithm is proposed to solve it since it is NP-hard. After comparing its results with those of the exact solver and other intelligent optimization methods in four cases, we conclude its competitive performance in both solution quality and efficiency.

With the growing adoption of circular economy principles in manufacturing, efficient disassembly and reassembly of end-of-life (EOL) products has become a key challenge in smart factories. This paper addresses the Disassembly and Assembly Line Balancing Problem (DALBP), which involves scheduling robotic tasks across workstations while minimizing total operation time and accounting for directional switching time between disassembly and assembly phases. To solve this problem, we propose an improved reinforcement learning algorithm, IQ(λ), which extends the classical Q(λ) method by incorporating eligibility trace decay, a dynamic Action Table mechanism to handle non-conflicting parallel tasks, and switching-aware reward shaping to penalize inefficient task transitions. Compared with standard Q(λ), these modifications enhance the algorithm’s global search capability, accelerate convergence, and improve solution quality in complex DALBP scenarios. While the current implementation does not deploy live IoT infrastructure, the architecture is modular and designed to support future extensions involving edge-cloud coordination, trust-aware optimization, and privacy-preserving learning in Industrial Internet of Things (IIoT) environments. Four real-world disassembly-assembly cases (flashlight, copier, battery, and hammer drill) are used to evaluate the algorithm’s effectiveness. Experimental results show that IQ(λ) consistently outperforms traditional Q-learning, Q(λ), and Sarsa in terms of solution quality, convergence speed, and robustness. Furthermore, ablation studies and sensitivity analysis confirm the importance of the algorithm’s core design components. This work provides a scalable and extensible framework for intelligent scheduling in cyber-physical manufacturing systems and lays a foundation for future integration with secure, IoT-connected environments.

Assembly lines, generally speaking, can reduce production costs, shorten cycle times, and achieve higher quality levels. Since the current market is characterized by increasing product variability, mixed-model assembly lines, in which similar product models can be assembled simultaneously, are more suitable to respond to varied market demands than traditional single-model assembly lines. In addition, in an assembly line, tasks often differ in processing requirements, and workers may have different qualification levels. This study, therefore, aims to construct models for the multi-objective mixed-model assembly line balancing problem with hierarchical worker assignment (MO-MALBP-HW). The goal is to generate a suitable plan for a mixed-model assembly line balancing problem considering the constraint of a hierarchical workforce, the cost of a hierarchical workforce, and production cycle time. When the problem is simple, it can be solved by a mixed integer programming (MIP) model. When the problem becomes complex, it can be solved by a multi-objective genetic algorithm (MOGA) and a non-dominated sorting genetic algorithm II (NSGA-II) to obtain a near-optimal solution. The implementation of this model can effectively manage the multi-objective mixed-model assembly line balancing plan, thereby improving plant efficiency and reducing cost.

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Human-robot collaboration (HRC) on assembly lines enhances productivity and human operators' well-being by enabling humans and robots to perform tasks independently or collaboratively in shared workspaces. Aligning with the human-centric vision of Industry 5.0, this study addresses the human-centered assembly line balancing problem with human-robot collaboration (HCALBP-HRC). A multi-objective mixed-integer linear programming (MILP) model is proposed to minimize cycle time, cognitive load, and ergonomic risk through effective task allocation and resource management. Experiment results of three groups of instances with different production scenarios verified the feasibility of the MILP model. The analysis of trade-offs among the three objectives under varying weight configurations highlights the conflicts between production efficiency and human-centered considerations, underscoring the importance of balanced decision-making in HRC-based assembly systems.

As manufacturing grows, factories recycle and reuse old items to save costs and reduce waste. This requires disassembly and assembly lines to collaborate, forming integrated disassembly-assembly lines. The disassembly and assembly line balancing problem (DALBP) is a complex NP-hard issue that involves effectively balancing and optimizing both disassembly and assembly tasks. A mathematical model is developed to minimize operation time for similar tasks. Validated using the CPLEX solver, the proposed algorithm demonstrates superior performance in addressing DALBP across four randomly selected cases when compared with other intelligent algorithms.

In this paper, we address the inherent limitations in traditional assembly line balancing, specifically the assumptions that task times are constant and no defective outputs occur. These assumptions often do not hold in practical scenarios, leading to inefficiencies. To address these challenges, we introduce a framework utilizing an"adjusted processing time"approach based on the distributional information of both processing times and defect occurrences. We validate our framework through the analysis of two case studies from existing literature, demonstrating its robustness and adaptability. Our framework is characterized by its simplicity, both in understanding and implementation, marking a substantial advancement in the field. It presents a viable and efficient solution for industries seeking to enhance operational efficiency through improved resource allocation.

By incorporating the uncertainty and imprecision inherent in real-world production systems, this paper introduces the multi-manned assembly line balancing problem (MMALBP) with fuzzy stochastic task processing times. MMALBP has recently emerged as a key challenge in flow-line production systems manufacturing large-scale products (e.g. the automotive industry), where multiple workers collaborate at the same station to perform different operations simultaneously on a single product. MMALBP is a decision problem that involves partitioning assembly tasks among stations and scheduling them across multiple workers while optimising key operational objectives related to capacity and/or operational cost of the line. To enhance realism and decision-making, a new problem termed fs-MMALBP is introduced, which models task time uncertainties as fuzzy stochastic variables. The objective is to optimise three conflicting criteria: (1) minimising the number of the stations, (2) minimising the total number of the workers employed along all the stations and (3) maximising the workload smoothness across the line. Given the NP-hard nature of the problem, a new robust multi-objective genetic algorithm (MOGA) is developed to identify the Pareto-optimal set. Simulation results show that MOGA effectively produces well-distributed Pareto-optimal solutions under fuzzy-stochastic uncertainty, with improved hypervolume and competitive CPU times across benchmark instances.

PurposeIn this study, the grey system theory is used for the first time to eliminate the uncertainties in the line balancing problem of U-type assembly lines, which are the most sensitive lines that can be easily affected by systemic and environmental parameters. Task times and learning effect are encoded with grey numbers for the U-type line balancing problem.Design/methodology/approachA 0–1 mathematical model is developed by considering the grey learning effect and grey task times within the framework of grey system theory, which aims to minimize the number of workstations (Type-1) by considering the uncertainty of the learning effect and task times.FindingsFindings highlight the fact that by considering the grey learning effect and grey task times for U-type assembly lines, the number of required workstations can be decreased, while line efficiencies can be increased.Originality/valueThis study makes a significant contribution to the literature by introducing the novel application of grey theory and grey numbers to handle uncertainties in task times and learning effects within U-type assembly line balancing problems.

Purpose: The purpose of this paper is to solve a grey parallel assembly line balancing problem with type-I (G-PALBP-I) to minimize the number of stations under the major constraints and restrictions of the PALs. Design/methodology/approach: A manufacturing system with parallel assembly lines (PALs) consists of at least two assembly lines placed next to each other in the facility layout. To design real-life PAL applications, the processing durations of the tasks may not always be fixed due to workers getting tired or making mistakes. In addition, the variability in customer demands may also affect the cycle duration called the total processing duration of a station. To better reflect the real-life applications of PALs, task and cycle times are expressed with grey system theory and grey numbers. A binary integer linear programming model is proposed to solve the G-PALBP-I. Findings: The proposed model is implemented to the PAL systems designed by using a simple assembly line data in the literature. The results show that considering precedence relationships and variability in task and cycle durations provides a more flexible and consistent perspective. Originality/value: The grey system theory and grey numbers, to the best of the authors’ knowledge, have not been considered to describe the uncertainty of task and cycle times in PALBPs. Therefore, this study provides important insight to both researchers and decision-makers in practice.

With the diversification of market demand, various mixed-model production methods have been widely adopted. Aiming to address the joint optimisation of mixed-model and parallel two-sided assembly line balance, a joint optimisation model of mixed-model and parallel two-sided assembly line balance is established. A discrete hybrid artificial fish swarm algorithm based on the genetic algorithm with crossover and mutation rules is proposed to solve this model. By computing 25 groups of classical examples, the results are compared with those of other heuristic algorithms and the mixed-model two-sided assembly line balancing problem; Comparison results show that the discrete hybrid artificial fish swarm algorithm is competitive in solving this problem, verifying the effectiveness of the model and algorithm; The balancing results of the parallel two-sided assembly line are superior to those of the two-sided assembly line, which verifies the effectiveness of the multi-line synergy effect in the parallel two-sided assembly line.

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Modern cyber–physical systems (CPSs) and IoT-enabled smart factories rely on human–robot collaboration (HRC) to combine human intuition and robotic precision in real time. Balancing such HRC assembly lines, where each task may execute in human-only, robot-only, or collaborative modes, poses a combinatorial challenge that defies scalable mixed integer linear programming (MILP) and oversimplified heuristics. In this letter, we present IBSHRC, a proof-of-concept Iterative Beam Search framework designed for single-product, straight-line CPS assembly systems. IBSHRC leverages mode-aware initialization, binary-search cycle-time refinement, and efficient pruning to navigate vast scheduling spaces at the network edge. On benchmark instances up to 100 tasks, our method delivers near-optimal cycle times with up to $300\times $ speed-ups over MILP (subsecond runtimes), demonstrating its promise for real-time, IoT-driven industrial scheduling.

This study, proposes a heuristic algorithm to balance Robotic Assembly Lines (RAL). A flexible line is assumed in which robots can be allocated to any station, perform any task, and have fixed setup costs. To consider both robot allocation costs and limiting the number of stations, the current work aims to minimize the system cost, which includes new station and robot allocation costs. It evaluates the performance of the algorithm with a large set of randomly generated samples and conducts statistical analyses to summarize, compare, and draw conclusions. The experimental results demonstrate the efficacy of the proposed algorithm in addressing large-scale problems in a reasonable timeframe.

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Assembly lines are still one of the most used manufacturing systems in modern-day production. Most research affects the building of new lines and, less frequently, the reconfiguration of existing lines. However, the first is insufficient to meet the reconfigurable production paradigm required by volatile market demands. Consequent reconfiguration of resources by production requests affects companies’ competitiveness. This paper introduces a problem-specific genetic algorithm for optimizing the reconfiguration of a Robotic Assembly Line Balancing Problem with Task Types, including additional company constraints. First, we present the greenfield and brownfield optimization objectives, then a mathematical problem formulation and the composition of the genetic algorithm. We evaluate our model against an Integer Programming baseline on a reconfiguration dataset with multiple equipment alternatives. The results demonstrate the capabilities of the genetic algorithm for the greenfield case and showcase the possibilities in the brownfield case. With a scalability improvement through computation time decrease of up to ∼\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim $$\end{document}2.75×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document}, reduced number of equipment and workstations, but worse objective values, the genetic algorithm holds the potential for reconfiguring assembly lines. However, the genetic algorithm has to be further optimized for the reconfiguration to leverage its full potential.

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Assembly lines are widely used mass production techniques applied in various industries from electronics to automotive and aerospace. A branch, bound, and remember (BBR) algorithm is presented in this research to tackle the chance-constrained stochastic assembly line balancing problem (ALBP). In this problem variation, the processing times are stochastic, while the cycle time must be respected for a given probability. The proposed BBR method stores all the searched partial solutions in memory and utilizes the cyclic best-first search strategy to quickly achieve high-quality complete solutions. Meanwhile, this study also develops several new lower bounds and dominance rules by taking the stochastic task times into account. To evaluate the performance of the developed method, a large set of 1614 instances is generated and solved. The performance of the BBR algorithm is compared with two mixed-integer programming models and twenty re-implemented heuristics and metaheuristics, including the well-known genetic algorithm, ant colony optimization algorithm and simulated annealing algorithm. The comparative study demonstrates that the mathematical models cannot achieve high-quality solutions when solving large-size instances, for which the BBR algorithm shows clear superiority over the mathematical models. The developed BBR outperforms all the compared heuristic and metaheuristic methods and is the new state-of-the-art methodology for the stochastic ALBP.

As an emerging technology, human-robot collaboration (HRC) has been implemented to enhance the performance of assembly lines and improve the safety of human workers. By integrating the advantages of human workers and collaborative robots (cobots), HRC enables production systems to process tasks consecutively, concurrently, or collaboratively. However, the introduction of cobots also makes the corresponding human-robot collaborative assembly line balancing problem more complex and difficult to solve. To solve this problem, we first propose an enhanced mixed integer program (EMIP) with various enhancement techniques and tighter bounds, and then, we develop an improved combinatorial Benders decomposition algorithm (Algorithm ICBD) with new local search strategies, Benders cuts, and acceleration procedures. To verify the effectiveness of our proposed model and algorithms, we conduct extensive computational experiments, and the results show that our proposed EMIP model is significantly better than the existing mixed integer program model; the percentages of instances that can obtain feasible and optimal solutions are increased from 82.42% to 100% and from 29.17% to 43.5%, respectively, whereas the average gap is decreased from 19.81% to 5.64%. In addition, our proposed Algorithm ICBD can get 100% of feasible solutions and 65.92% of optimal solutions for all of the test instances, and the average gap is only 1.49%. Moreover, compared with existing Benders decomposition methods for this problem, our approach yields comparatively better solutions in notably shorter average computational time when run in the same computational environment. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This research was supported by the National Natural Science Foundation Council of China [Grants 72401214, 92167206, 7221101377, 72471169, and 72231005], the Ministry of Education of China [Grant 24YJC630078], and Computation and Analytics of Complex Management Systems (Tianjin University). This research was also supported by the Tianjin Natural Science Foundation Project [Grant 23JCQNJC01900] and the Tianjin Philosophy and Social Science Planning Project [Grant TJGL21-016]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0279 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0279 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

The Simple Assembly Line Balancing Problem with Power Peak Minimization (SALB3PM) is a relatively new problem that aims to assign tasks to workstations with a focus on minimizing power peaks. By integrating load balancing and task scheduling, this problem offers a comprehensive approach to enhancing energy efficiency in production systems, which can lead to significant cost savings alongside a positive environmental impact. This paper introduces novel models for SALB3PM based on Maximum Satisfiability (MaxSAT), the natural optimization extension of the Satisfiability problem, providing a new perspective to solve this optimization problem effectively. Experimental results demonstrate the efficiency and robustness of our approach with respect to the MaxSAT solvers applied. To the best of our knowledge, this is the first attempt to address the SALB3PM problem through the lens of Maximum Satisfiability.

Incorporating ergonomic considerations into an assembly‐line balancing problem (ALBP) enhances productivity and minimizes ergonomic concerns. The assembly process, characterized by repetitive motions and handling numerous components, can lead to worker overload. Consequently, the inclusion of ergonomic aspects results in an appropriate distribution of assembly operations and relative workloads. This study investigates a multi‐objective ALBP aimed at minimizing the number of workstations, overall skill level required, and variance in workers' energy expenditure across workstations. To address the ALBP while considering the ergonomic aspects, this study proposes an approach based on the non‐dominated sorting genetic algorithm II (NSGA‐II) and multi‐objective simulated annealing (MOSA) using Pareto optimality. A comparative analysis of the NSGA‐II and MOSA is conducted in single‐ and multiproduct production scenarios, and a computational study involving various factors is performed to identify the dominant algorithm. The computational analysis indicates that the runtime performance of MOSA is 73.287% better than that of NSGA‐II; therefore, MOSA outperforms NSGA‐II. This study aims at applying scientific knowledge concerning manufacturing ergonomics to assist manufacturing industries in enhancing their productivity.

This paper presents a declarative framework for resilient machining line planning, integrating line balancing and disruption handling within a unified, interactive decision-support environment. Building upon earlier constraint-based models, the proposed approach incorporates sustainability-oriented improvements through Pareto-based multi-criteria optimization. The model supports trade-off analysis across cost, energy consumption, tool wear, and schedule continuity, enabling predictive planning and adaptive dispatching under operational uncertainty. By combining proactive balancing with reactive disruption handling in a single declarative formulation, the framework addresses a key gap in the current production engineering methodologies. A case study employing real data and real-world-inspired disruption scenarios demonstrates the effectiveness of the approach. Compared to traditional sequential strategies, the framework yields superior performance in terms of solution diversity, responsiveness, and sustainability alignment, confirming its value for next-generation, resilient manufacturing systems.

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ABSTRACT Most existing studies about line balancing problems mainly focus on disassembly and assembly separately, which rarely integrate these two modes into a system. However, as critical activities in the remanufacturing field, assembly and disassembly share many similarities, such as working tools and processing sequence. Thus, this paper proposes a multi-objective hybrid production line balancing problem with a fixed number of workstations (HPLBP-FNW) considering disassembly and assembly to optimise cycle time, total cost, and workload smoothness simultaneously. And a novel Pareto-based hybrid genetic simulated annealing algorithm (PB-HGSA) is designed to solve it. In PB-HGSA, the two-point crossover and hybrid mutation operator are proposed to produce potential non-dominated solutions (NDSs). Then, a local search method based on a parallel simulated annealing algorithm is designed for providing a depth search around the NDSs to balance the global and local search ability. Numerical results by comparing PB-HGSA with the well-known algorithms verify the effectiveness of PB-HGSA in solving HPLBP-FNW. Moreover, the managerial insights based on a case study are given to inspire enterprise companies to consider hybrid production line in the remanufacturing process, which is beneficial to reduce the cycle time and total cost and improve the service life of the equipment.

PT. Risa Implantama is a company engaged in the production of bone implants that focuses on the production of bone pins and screws. This company produces bone pins with various types and then markets the production results through distributors and also produces goods according to incoming orders, both from distributors and hospitals. Rapid advances in medical science and technology have significantly improved the quality and longevity of human life. The field of orthopedics in the world of medicine has also experienced technological advances in dealing with various cases in its field, one of which is bone fractures. Based on initial calculations, the line efficiency was 51.07%, then the balance delay was 48.93% and the idle time was 1.90 minutes. After processing the data using the ranked positional weight method for the line efficiency of the broad plate production process, the line efficiency results were 64.42%, then the balance delay was 35.58% and the idle time was 14.64 minutes. Furthermore, after data processing using the region approach method for the line efficiency of the broad plate production process, the line efficiency results were 54.47%, then for the balance delay of 45.53% and idle time of 14.64 minutes. Based on the calculation results, the Ranked Positional Weight (RPW) method is the optimal method in solving line balancing, because it has the highest Line Efficiency (LE) value and the lowest Balance Delay (BD).

High productivity. Production line is one of the commonly used production methods in manufacturing enterprises. Improving the overall efficiency and production capacity of production line, reducing production tempo and pursuing production synchronization are more and more valued by manufacturing enterprises. Refrigerator is one of the main products of H company, which is produced by the way of production line. The investigation found that the capacity of H company’s refrigerator box is insufficient, and the production efficiency needs to be improved. Aiming at this problem, this paper takes the refrigerator box production line of H company as the research object to study its balance problem. Using 0-1 integer programming, the mathematical model is constructed with the goal of minimizing the production time, and the LINGO software is used to solve it. The production line operation elements are redistributed, and the balance rate of the production line is improved to a certain extent after optimization.

Background. Production line balancing is a key element in improving the capacity and stability of manufacturing systems, yet conventional practices often ignore the elastic relationship between production intervals, capacity, and resource structure. This research develops and formalizes Rubber Ball Theory, a theoretical approach that views production systems as elastic entities, where changes in one operational variable trigger compensatory responses in other variables. Aims. The objective of this research is to develop a mathematical model for interval-based production line balancing and analyze its impact on overall supply chain performance. Methods. The research methodology includes developing an interval-based line-balancing optimization model, integrating concepts of bottlenecks and elastic capacity planning, and conducting empirical testing through a manufacturing industry case study. The developed model minimizes the system interval as the primary control variable, accounting for capacity constraints, precedence relationships, and parallel machine configurations. Sensitivity analysis is performed to evaluate the system's response to changes in the target interval and the number of parallel resources. Result. The results show that emphasizing production intervals without adjusting structural capacity leads to system instability and bottleneck displacement, while an elastic approach based on Rubber Ball Theory can sustainably increase production capacity. Furthermore, this approach has been shown to improve the reliability, responsiveness, and efficiency of asset management in the supply chain and contribute to reducing the variability of production flows that trigger the bullwhip effect. Conclusion. The main contribution of this research is the provision of an integrated conceptual and mathematical framework linking production line balancing to supply chain performance through elastic management of production intervals. Implication. These findings provide theoretical and practical implications for designing more adaptive and sustainable production systems and capacity planning.

Line balancing (LB) in production processes is a crucial part of optimizing workstations, improving efficiency and productivity in the industrial environment. Over the years, various methods and applications, both theoretical and practical, are developed to achieve this goal. However, while there are numerous studies in line balancing optimization, little attention has been paid to its effective validation in real industrial environments. Consequently, it is essential to propose a methodology that integrates both aspects: the theoretical development of solutions and the empirical validation of their effectiveness in practice. This paper presents the case of the final assembly process of an electronic board for a metal detector, based on real data, focusing on the need identified in the literature and proposing a comprehensive methodology to validate the implementation of line balancing. The Integrated Method for Process Optimization and Validation (IMPOV) methodology is proposed, which validates the implementation of line balancing through a Value Stream Mapping (VSM) approach and simulates the process using FlexSim 2024® software. Finally, the optimal solution is validated with the help of the multi-criteria method Multi-Objective Optimization by Ratio Analysis (MOORA). Through the application of this methodology, the final assembly line was optimized, eliminating five operating positions and significantly reducing idle time from 79.16% to 14.06%. As a result, the utilization of process resources increased from 27.84% to 85.95%, which in turn increased production from 5,578 to 9,393 units. This methodology provides professionals in the industrial sector with an effective tool for optimizing assembly processes, ensuring the best alternative for the efficient use of each resource before physically implementing any changes, identifying areas of opportunity and correcting them in advance. This contributes to achieving higher levels of competitiveness and ensuring permanence in the global market.

Assembly and disassembly are important activities in the manufacturing/remanufacturing process. Although the line balancing problems of them have been extensively discussed in the existing literature, they are rarely integrated into one system. In this paper, a hybrid production line balancing problem is adopted while considering the similarity between the assembly and disassembly tasks. First, to better reflect the uncertainty existing in the actual production environment, a mathematical model of the multi-objective stochastic hybrid production line balancing problem is presented, in which task disassembly times are assumed to be random variables with known normal probability distributions. Then, a hybrid VNS-NSGA II algorithm combining variable neighbourhood search (VNS) and non-dominated sorting genetic algorithm II (NSGA II) is proposed to solve the problem. VNS is embedded into NSGA II as a local search to improve the quality of the solutions found by the NSGA II at each generation. Finally, the effectiveness of the proposed method is verified by a case study, and the superiority of hybrid production line is reflected by comparing the solutions of the independent production line with the hybrid production line. Computational comparisons demonstrate the potential benefits of the hybrid production line and the proposed method.

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The automotive industry, in the form of electric cars, has entered Indonesia, making competition in automotive companies increasingly competitive, including the companies in this study that have had a positive impact, namely increasing demand for car batteries. In line with the development of electric cars, the company also developed new products, so the company installed an assembly line. but after the assembly line was running, productivity from production capacity and efficiency could not reach an average output of 823 units/day, the actual average output produced was only 622 units per day. There is a problem with line balancing on the assembly line which causes quite high waiting times at several stations, does not achieve production targets, and makes line efficiency less than optimal. Therefore the purpose of this study is to make improvements with the right Line Balancing method to be applied to achieve a suitable line balance and to find the Line Efficiency value that can be gained from the application of Line Balancing and how much the production capacity increases. The results of the research show that the Largest Candidate Rule method is the best method because it can increase the Line Efficiency value by 91.03%, the percentage of idle time or Balance Delay has improved, which has decreased to 8.97% and the total idle time is 21.3 seconds compared to the initial conditions. And can increase production capacity per day by 35.7%. This will be achieved consistently if the company can continue to improve employee skills by conducting training and increasing operators' motivation.

The development of manufacturing industry is currently experiencing very rapid development, although the Covid-19 pandemic is still ongoing, this development cannot be inhibited. PT. Madya Putera Tehnik is an industry-engaged automotive part manufacturer that produces bushings. The problem experienced is that the high demand for bushing rubber products makes companies have to optimize the performance of employees and their machines so that targets can be met on time. This study aims to analyze the effectiveness of the production line in the process of making rubber bushings as well as to provide suggestions regarding the balance of the track to increase productivity by using the Theory of Constraints and Heuristic methods. The Theory of Constraints method aims to identify bottlenecks at the outer pipe cutting, rubber roll, rubber clean, and packing work stations that are experiencing problems, and the Heuristic method aims to calculate the balance of the production line. The results obtained from data processing, namely the ranked positional weight and largest candidate rules method have the same results with the number of work stations as many as 14 work stations, track the efficiency of 70%, balance delay of 30%, smoothing index of 66.57, and total exits as many as 1145 units of rubber bushings. It can be interpreted that the two methods have proposed the most optimal trajectory conditions. The company should review the balance of the track and the current production capacity of the company.

The production line system is an important problem for the majority of manufacturing industries in Indonesia. PT XYZ is manufacturing company focuses on general construction and mining products supplier. Friction Bolt Stabilizer is one PT XYZ’s superior products. The problem in the friction bolt stabilizer production line is unbalanced distribution of work elements. Based on these problems, in this study a solution is provided by comparing the actual production line with the results of line balancing analysis. There are two methods used, namely Ranked Positional Weight and Killbridge Western Heuristic. The result is that the Killbridge western heuristic method produces the best design with a performance level of 91% line efficiency, 9% balance delay, a total of four workstations and a smoothness index value of 401.55 seconds

This research aims to improve the efficiency of an automotive engine production line by reducing waiting waste caused by an unbalanced workload. Currently, there are 94 tasks allocated to 8 workstations. The cycle time of the bottleneck workstation, which is 167.60 seconds per unit, is higher than the takt time set at 87 seconds per unit. The tasks in the production line have been divided into 3 groups due to limitations in space and machinery. The tasks in each group will be balanced separately. The tasks in groups 1 and 3 can be arranged on the workstations to achieve a cycle time lower than the takt time, with the same number of workstations. To balance the assembly line in these groups, a mathematical model is used with the objective of minimizing cycle time under the constraint of a fixed number of workstations and a cycle time not exceeding the takt time. However, the other group does not consider the constraint of a cycle time lower than the takt time because the company does not want to increase the number of existing workstations, which is currently insufficient to meet this constraint. The Branch and Cut algorithm using the COIN-OR CBC library in OpenSolver is used to solve the mathematical models. The experimental results show that the production line balancing efficiency has improved for all groups, with an increase from 70.35% to 96.56% for group 1, an increase from 81.52% to 99.91% for group 2, and an increase from 90.42% to 98.92% for group 3. The lead time is reduced from 235.18 minutes to 198.51 minutes.

No abstract available

In the current era of globalization, business competition demands industries in manufacturing to improve their business strategies are increasingly. The company that produces spring beds of various sizes has experienced rapid development. Spring bed 180 cm x 200 cm size has the longest process and research on the springbed production line is 180 cm x 200 cm. The company has 9 work stations with 19 work elements with time variation of work elements which are 50 seconds to 2737 seconds. The problem experienced by the company is the bottleneck at the assembly station because it has the longest cycle time of 2737 seconds. One method used in assembly line balancing is the RPW method (Ranked Positional Weights). The RPW method is an approach to solving problems in line balance and finding solutions quickly by the number of workstations determination at a minimum by giving weight to each work elements that has been placed on all work elements. Using the RPW method, obtained line efficiency, balance delay and smoothing index were 86.09%, 13.91% and 1418.45 with the number of work stations is 8.

This paper focuses on assembly line balancing (ALB) problem which the objective is to maximize the assembly line efficiency. The problem solved using a greedy heuristic method. MATLAB Software is used to perform the proposed greedy heuristic method. Then, the proposed method is applied to a previous real life case problem that is found in literature for the cookers assembly line in the Light Industrial Company in Iraq. The results of the proposed method is compared with the performance of the company without applying any assembly line balancing method. The result of this paper is also compared with the results obtained from a previous work in the literature which is based on Shortest Operation Time (SOT) using the software named Quantitative Methods, Production and Operations Management (POM-QM). The outcomes of the research have shown that the greedy heuristic method is more efficient, where the efficiency increased from 78.24% before applying assembly line balancing and 81.64% for the previous work based on SOT method to 85.53% when applying the greedy heuristic method. The research has recommended that the decision maker in the company should follow the most appropriate method to achieve a high efficiency operations.

The development of the industry forces the performance of manufacturing companies to be managed according to high standards at all levels such as cost, quality, and speed. Strong performing companies will generate growth in various aspects of global competitiveness. which ultimately affects performance in development, investment, trust from shareholders, In Indonesia the manufacturing industry has a very important role in economic development and contributed to national income by 22% in 2016 (Bank Mandiri 2018) and is also the second-largest car manufacturing industry in Southeast Asia. The biggest challenge today is a company not only competing with competitors who are different but also with the same group in various countries and even making alliances to maintain competitiveness carried out by Hyundai - KIA, Renault-Nissan-Mitsubishi, etc, To gain the trust of top management so that they are given new model or technology, on their production systems around the world including manufacturers in Indonesia, this is done so that companies are competitive, one of which is in terms of optimizing the number of workers. The purpose of this research is to improve performance in terms of the production line at the assembly line at an automotive factory in Indonesia by using several actual data collection methods Line Balancing using the Yamazumi chart and calculation by Moodie Young The results of this research contribute to the literature in the automotive sector. but in various other sectors.

XYZ company has a problem on the production line that is the build up of work stations and bottlenecks that indicate an imbalance. The aim is to solve the problem of balancing the production line at the company. The production line using the Genetic Algorithm method is done by determining objective parameters and functions, making the encoding of the work station, determining the initial initialization according to the actual production path, then doing iteration with selection, crossover, and mutation to form a new population, and ending with the termination of the algorithm. The results of the actual production line obtained are the number of work stations as many as 8 work stations and with the largest cycle time of 2213 seconds. The genetic algorithm production trajectory is obtained after achieving the maximum objective function value or until the maximum iteration limit is reached and consists of 7 work stations with the largest cycle time of 2203 seconds, and has a higher efficiency value thereby minimizing idle time, having a higher value of balance delay low so that it shows a decrease in waiting time and a lower value of the smoothing index compared to the actual production line where the production line is more balanced, meaning that the division of work elements is quite evenly distributed on the assembly line.

PT. Bogatama Marinusa is one of the fishery industry sectors located in South Sulawesi that moves in shrimp processing and frosting as pioneer of fishery industry in southern Sulawesi. PT. Bogatama Marinusa certainly wants the planned production target to be achieved as expected, where every day the company processes raw materials with the capacity of approximately 575 kg for vannamei shrimp and 425 kg for black tiger shrimp. At deheading work station (shrimp head separation) and sortation area (sorting) materials buildup occurs in the production flow (bottle neck) which results an idle time to other work stations. With the existence of these problems, it is necessary to do production line balancing in order to minimize idle time and be able to balance the production lines so that the works carried out effectively and efficiently. The method used for overcoming the production line imbalance is by applying line balancing, namely the heuristic method, which is calculation using Regional Approach (RA) and Ranked Positional Weight (RPW) methods. Line efficiency (LE) increased by 83.33%, smoothing index (SI) decreased by 160.90, balance delay (BD) decreased by 16.66%, and idle time (IT) decreased by 264 minutes, and production capacity could be increase to 1,022 kg means that the results are far better than the current conditions.

Waste in the production process causes waste of resources and resources while not creating real value, which is a major threat, reducing the competitiveness of enterprises. One of the measures that can both improve productivity and reduce costs effectively for enterprises is line balancing. Line balancing effectively maximizes idle time at stations, minimizes the number of workstations as well as uses fewer workers and equipment while still ensuring the company's production output and significantly increases production efficiency. The article focuses on the production line of Vancover Dining chair backrest clusters at Thanh Thang Limited Company. The actual production line still has some problems such as many stages of semifinished products and too much idle time at some other stages. Therefore, the study proposes a method of rearranging the line in a Ushape to balance the production line to optimize the production process, but also from eliminating non-value-added activities. By reducing waste, enterprises not only save costs but also achieve increased output and shortened production time.

Line balancing is required in the production process. Without a balanced production line at work stations, the production process is less effective and efficient. Refinery at PT. Smelting has a type of mass production process, so it is planned to determine the optimal production line so that the loading on each work station will be more evenly distributed and reduce idle time. The method used is the measurement of working time with a stop watch. the data used is the work element and the time needed by the operator to complete the copper cathode production process which is from the anode arriving at the Anode Preparation Machine (APM) until the copper cathode is finished strapping at the Cathode Washing & Stripping Machine (CWSM). There are two results of the analysis,based on the first analysis that by using the line balancing method, the company can achieve a line efficiency of 78.10% and reduce the balance delay by 15.11% which is from 37.01% to 21.90%.

In order to solve the problems of complicated process types, long production process arrangement time and low production efficiency of national clothing production, a national clothing production line is constructed based on a hanging assembly line. Firstly, the national clothing production line is thoroughly explored. Considering the constraints of the production line and the multi-objective optimisation, a mathematical model of balance optimisation is constructed. Secondly, the adaptive improvement of the genetic algorithm (GA) in coding and genetic operation is explored. On this basis, the tabu search algorithm is embedded into the GA framework, and the genetic tabu hybrid algorithm is proposed to realise the production balance of national clothing. MATLAB software is used to solve the problem of balancing the production line process arrangement. Finally, the Flexsim simulation software is used to simulate the actual production process to verify the intelligent arrangement results, and an application analysis is carried out with a Mongolian clothing with a national clothing characteristic manufacturing process as an example. The results show that the genetic tabu hybrid algorithm is suitable for solving the problem of process arrangement balance in the national clothing production line. The assembly line equilibrium index after intelligent automatic arrangement of the genetic tabu hybrid algorithm was 6.6, the maximum beat and the minimum beat difference Fmin was 19. The simulation model verifies that the stable operation time of each station accounts for more than 95%, and the simulation results show that the arrangement scheme is feasible.

Production line balancing is done to increase line efficiency. Product flow between workstations is stated to be better when the percentage of line efficiency is higher. One of the factors that can hinder the smooth running of a line is a bottleneck. The occurrence of a bottleneck at the Ribbed Smoked Sheet production station, namely the milling and sorting station, at PT. Wabin Jayatama caused the production line to become unbalanced. The output that will be generated from this research is the efficiency level of the actual production line and the proposed design of a production line with more optimal efficiency, which will later be used as a consideration for recommendations to improve the performance of the company's production line. This research was carried out using the Kilbridge & Wester Heuristic method and the Ranked Positional Weights Heuristic method. The results of the line balance analysis showed that the production line had a higher line efficiency (LE) of 50.05%, an increase by 25.62% to 75.67%. The initial line balance delay (BD) of 49.55% can be reduced by 25.22% to 24.33%. Then the Smoothest Index (SI) of 447.74 can decrease by 360.51 to 87.23.

In the manufacturing industry, the balance of the production trajectory is an important factor in increasing productivity and efficiency. In the production process at PT XYZ, there are problems with the balance of the production trajectory at the workstation, so it is necessary to determine the optimal production trajectory so that the load on the workstation can be evenly distributed and able to reduce idle time. The Ranked Positional Weight (RPW) and Region Approach (RA) methods were chosen in this study. The main bottleneck was found at the hole cutting stage, where tool limitations caused output differences between manual and machine processes. This imbalance resulted in idle time which affected the overall production efficiency. The analysis results show that the application of RPW and RA methods is able to improve trajectory efficiency by optimizing the number of work stations and reducing idle time. The analysis results state that the company can achieve a trajectory efficiency of 88.48% and reduce the balance delay from -16.67% to 15,73% and a smoothness index of 611,25%.

In this study, an oven assembly line that is planning to re-establish manufacturing to increase the efficiency of the assembly process. The importance of the problem emerges from a real-world application consisting of product-oriented restrictions. These multiple restricted problems address the single model assignment restricted ALB problem with positional constraints. A cost-based objective function is used to cope with this problem. The number of platformed and non-platformed stations, the number of direction changes in a station, the number of stations in which both connector and combiner are used are the cost factors of the objective function. Also, the main objective of the problem is to minimize the total number of stations while satisfying the restrictions. A simulated annealing-based hyper-heuristic is adapted and applied to the balancing problem of oven manufacturing process with assignments and operational restrictions with multiple objectives. The results show that better solutions can be found in the current line balance level while satisfying more restrictions. It is also observed that line balance can be improved depending on the relaxation of the restrictions.

Production line is a set of sequential operation that support refinery process product from raw material to end process into finish product. However, due to the operations have different time process to finish an item/part, it caused unbalance processing time in certain work station in a production line. This problem increases overtime to work station with heavy load tasks, in the contrary to work station with low load tasks results idle workers. Overtime is definitely cost to organization. PT. Metindo Era Sakti has overtime as their issue. To balance the process time in work stations, line balancing method is one of option to the problem. Line Balancing uses to reduce overtime by increasing production line efficiency, reducing delay time, and decreasing worker idle time [8]. By using Rank Positioned Weight (RPW) and Largest Candidate Rules (LCR) and comparing those methods was proven that production line efficiency increased by 85,63%, delay time decreased by 14,37%, and reduced idle time by 3,77 minutes. Therefore, while reducing overtime, PT. Metindo Era Sakti enable to minimize overtime cost by Rp. 4.092.000 each year.

At the moment industrial development, there is open competition on a national and international scale, the manufacturing and service industry sectors are developing very quickly. To create good, quality products and efficiency, the company must have a good track balance. As is the case in nail production. CV. XYZ is a company engaged in manufacturing. This company is located in the city of Sidoarjo, East Java. The aim of this research is to improve the balance of the work station trajectory of the nail production process at CV. XYZ. The method used in this research is the Region Approach and ranked positional weight. The best method chosen for this problem is the Region Approach method, because this method has the greatest line efficiency (68.17%), the smallest balance delay (31.83%), and the smallest smoothes index (100.63), so this method was chosen as the best method and can improve line balancing conditions in the company. The increase in line efficiency from 54.68% using the company method to 68.17% using the Region Approach method can occur due to a reduction in the number of work stations from 5 work stations as well as a decrease in the number of smoothing indexes from 168.20 to 100.63. which indicates that there is an increase in balance between work stations.

No abstract available

Business competition is increasing, companies must reorganize their business strategies and tactics every day in the era of globalization. The purpose of this study was to determine the effect of Line Balencing on the efficiency and effectiveness of shoe production at PT Wangta Agung. The research method used is descriptive research. Descriptive research is a type of research conducted with the main purpose of providing an objective description or description of the situation. The results of the research on the actual shoe production workstation configuration consisted of 15 work stations with a cycle time of 16 seconds. Analysis of sewing activity work time takes about 23.75 minutes for one additional operator to assist the two main operators in meeting the set targets. The increased capacity of the shoe-making production workstation consists of 7 stations that have a balance of 16 seconds of delay time with production efficiency with a yield of 87.5%.

The management of the production line is a challenging task due to the high level of uncertainty in demand, which can lead to unbalanced utilization of resources. This may result in a potential deterioration of management satisfaction in terms of cost-effectiveness. Therefore, it requires efficient tools to optimize resource utilization. With such inherent needs, this paper presents a simulation-based decision support framework for garments industries. The Discrete Event Simulation (DES) is used to model different scenarios for the operational processes. The procedure focuses on the line balancing technique, which aims to eliminate bottlenecks and optimize the production process by balancing the workload. The results of this study demonstrate the effectiveness of the line balancing technique in improving line efficiency, reducing the idle time of the operators, and increasing productivity. The simulation was developed using AnyLogic simulation software. The outcome of the process is thoroughly evaluated and justified using a case study.

Infrastructure development for Gresik Smelter Project requires a substantial amount of precast spun piles. The current factory capacity of 50 piles/day does not meet the minimum project's daily needs of 80 piles/day. The current production line still shows an imbalance output on each existing work station, which caused a numerous problems such as delays, bottle necks and ineffective production flows leading to a reduction in production capacity. This study is conducted using the Line Balancing method, Process Activity Mapping and Root Cause Analysis. Using this method, you can find out the existing conditions of each work station, understand the production flows and identify waste by grouping the stages of the production process into activities that are Value Added, Non Value Added and Necessary but Non Value Added. The Takt Time value needed is 15 minutes/unit, the results of this study indicate there are 4 work stations with Station Time above Takt time and have an NVA value of 14.71%. After analyzing and improving the 5 research variables the NVA value can be reduced to 4.58% and the overall Station Time has a value under 15 minutes/unit resulting on the fulfillment the target of 80 piles/day.

Ecological environment and natural resource issues are becoming more and more prominent, which promotes the recycling of waste products for green economy. Disassembly plays a key role in the remanufacturing and reuse of waste products. However, with the rapid development of production automation, designers tend to ignore the fact that manual operation is more flexible. It is of great importance to consider human factors in a disassembly process. This work considers two human disassembly postures, namely standing and sitting. The multiobjective disassembly line balancing problem considering human posture changes is studied. A mathematical model with the objective functions of maximizing profit, minimizing the number of posture changes at a workstation, and minimizing the difference of maximum posture changes between any two workstations is established. The model is solved through a newly proposed Pareto-based discrete harmony search algorithm. Three neighborhood structures are designed to enlarge the search space for better solutions. Furthermore, an elite reserve strategy is used to improve the global optimization ability of the proposed algorithm. Finally, the proposed model and algorithm are applied to cases of different scales of complexities, and the effectiveness of the proposed model and algorithm is verified in comparison with four competitive algorithms.

Considering the complexities, risks, and uncertainties of disassembling large end-of-life (EOL) products such as cars and buses, a two-sided human-robot disassembly line can utilise both sides of the workstations to enhance efficiency, improve safety, and increase revenue. This paper develops a human-robot cooperation two-sided partial disassembly line balancing model (TPDLB-HRC) to minimise energy consumption and maximise net revenue by addressing four interrelated sub-problems: planning disassembly sequences, selecting disassembly tasks, assigning tasks to mated-stations, and allocating human-robot resources. In addition, a new reinforcement-learning multi-objective evolutionary algorithm based on decomposition (NRL-MOEA/D) is developed, integrating an encoding/decoding scheme, reinforcement learning, problem characteristics, and coevolution between sub-problems to address the above challenges. The effectiveness and superiority of the designed NRL-MOEA/D in solving various cases are tested by comparing it with eleven algorithms. Finally, the applicability of the proposed method is verified by a series of EOL examples, and trade-offs are made under different recycling profits to guide decision-makers in constructing disassembly schemes in real situations.

Abstract This study presents an innovative software system for optimizing sewing line balancing in apparel manufacturing, addressing challenges posed by dynamic production demands, such as fluctuating order sizes and varying product styles. By Integrating real-time data with lean principles, the system utilizes a parallel station position-weighted algorithm and dynamically assigns operators based on current task metrics. This real-time data integration for operator assignment is the system’s key innovation. A case study demonstrated significant improvements: line efficiency increased from 79.68% to 88.31%, and per-operator output rose by 10%. These results highlight the potential for substantial efficiency gains in apparel manufacturing.

This study explores the implementation of lean manufacturing principles, specifically focusing on line balancing, in an automotive components industry to address productivity challenges such as inventory management, scrap reduction, and lengthy die exchange times. By optimizing the assembly line, the study aimed to reduce manpower and improve efficiency. The methodology involved calculating process cycle times, identifying non-value-added activities, and using tools like the Standardized Work Combination Table (SWCT), Value Stream Mapping (VSM), and Single Minute Exchange of Die (SMED) to streamline operations. Key findings revealed that the number of operators required per part was significantly reduced, leading to a theoretical reduction of 18 operators per day. This optimization not only minimized cycle time variations and die changeover times but also enhanced overall production efficiency, resulting in annual cost savings of Rs 28,08,000. The analysis demonstrated substantial improvements in line efficiency by addressing bottlenecks, balancing workloads, and implementing lean tools such as 5S, Kaizen, and visual management. The introduction of a more balanced and efficient production process reduced non-value-added activities and idle times, with Line Efficiency (LBR) improving on balanced lines (e.g., CFT Outer U129: 73% to 93%) and Process Efficiency (PE) in the stores, line segment increasing from 37.89% to 65.45% (+72.7% relative), alongside a 60 minute reduction in stock staging time at the PC zone. The case study underscores the potential of lean methodologies to significantly enhance productivity, reduce costs, and improve resource utilization in medium-scale manufacturing industries. These findings provide a valuable framework for other industries facing similar challenges, highlighting the importance of continuous improvement and sustainability in maintaining a competitive edge. The novelty of this study lies in the empirical integration of lean tools to address specific constraints within a mid-scale automotive supply chain, resulting in quantifiable manpower savings and cost reduction.

This study proposes an integrated smart manufacturing model aimed at im-proving Production Efficiency (PE) in the snack industry, focusing on a Peruvian fried corn chip (FCC) production line. It addresses critical problems caused by extended changeover times, unplanned downtimes, and idle times by workload imbalances. The methodology combines three industrial engineering tools: Single Minute Exchange of Die (SMED), predictive maintenance, and line balancing, with Machine Learning (ML) and Internet of Things (IoT) technologies. A simulation-based implementation using ARENA software evaluates the conceptual smart manufacturing model’s impact across three bottleneck stations: washer, fryer, and bagger. Results demonstrate a significant PE increase from 79.80% to 82.55%, surpassing the industry benchmark. Washer changeover time was reduced by 30.1%, Fryer downtime dropped by 44.9%, and Bagger idle time nearly matched the optimal level. Although residual troubles remain in Washer and Fryer stations, the improvements validate the model’s effectiveness in reducing nonvalue-added time. This study offers a novel, data driven framework that integrates traditional manufacturing tools with digital technologies to support industry 4.0 adoption in food processing environments. From a managerial perspective, the model serves as a practical guide for companies seeking to enhance PE, reduce costs and optimize resource use. Its value lies in addressing previously underexplored applications of smart manufacturing in the snack sector, bridging theoretical research with actionable outcomes.

XY Electronics, a leading international company in electronic components manufacturing, is confronting significant production constraints that adversely affect output, lead times, and operational expenses. This study examines the manufacturing line for product A using Value Stream Mapping to analyze process times and identify bottlenecks where takt times are exceeded. It focuses on areas surpassing production cycle times and aims to enhance line utilization through better line balancing and waste reduction. The results reveal that the header assembly, along with coplanarity and pre-testing 3, are major bottlenecks, which significantly impact productivity. By optimizing task allocation, refining workforce distribution, and employing cross-training, the production line efficiency improved significantly. In addition, strategic workforce reallocation and station optimization were crucial in addressing resource underutilization and enhancing overall operational efficiency

This study endeavors to enhance the productivity of solar module production in a renewable energy conversion company. The existing production line needs help in achieving the daily target of 64 units, resulting in an actual output of only 40 units. This study focuses on optimizing the production line by considering processing time adjustments and operator flexibility. To accurately measure working time, we incorporate operator flexibility and adjustment factors in the planning phase of solar module production. The primary objective is establishing an optimal production trajectory by balancing the load and capacity across workstations, thereby elevating overall production efficiency. The approach involves the application of the trial-error method and the shortest operation time method, implemented through POM QM software. Through meticulous data analysis, we determine that the trial-error process yields the most optimal results. Post-implementation, improvements are evident as the solar module production capacity increases to 60 units per day from the initial 40 units. Additionally, the idle time ratio on the assembly line to available time diminishes to 13.17% after optimization. This study contributes valuable insights into the effective enhancement of solar module production lines, emphasizing practical methodologies and software-assisted techniques for achieving substantial productivity gains in the renewable energy sector. 

With the sharp increase in the number of products and the development of the remanufacturing industry, disassembly lines have become the mainstream recycling method. In view of the insufficient research on the layout of multi-form disassembly lines and human factors, we previously proposed a linear-U-shaped hybrid layout considering the constraints of employee posture and a Duel-DQN algorithm assisted by Large Language Model (LLM). However, there is still room for improvement in the utilization efficiency of workstations. Based on this previous work, this study proposes an innovative layout of U-shaped and circular disassembly lines and retains the constraints of employee posture. The LLM is instruction-fine-tuned using the Quantized Low-Rank Adaptation (QLoRA) technique to improve the accuracy of disassembly sequence generation, and the Dueling Deep Q-Network(Duel-DQN) algorithm is reconstructed to maximize profits under posture constraints. Experiments show that in the more complex layout of U-shaped and circular disassembly lines, the iterative efficiency of this method can still be increased by about 26% compared with the traditional Duel-DQN, and the profit is close to the optimal solution of the traditional CPLEX solver, verifying the feasibility of this algorithm in complex scenarios. This study further optimizes the layout problem of multi-form disassembly lines and provides an innovative solution that takes into account both human factors and computational efficiency, which has important theoretical and practical significance.

No abstract available

The advances of manufacturing technology accelerates the replacement of consumer products. The recycling of these out-of-date products not only has economic benefits but also contributes to environmental protection. Therefore, the disassembly and reuse of products have attracted great attention all over the world. The traditional human worker disassembly is characterized by high cost and low efficiency. Robots can work more efficiently, but they are not flexible enough to perform different tasks. On the other hand, the combination of a U-shaped disassembly line and a single-row linear disassembly line would offer unique advantages for various applications. This work studies a hybrid disassembly line balancing problem (HDLBP) based on human-robot collaboration. The special challenge with HDLBP is that we need to consider the work load balancing among different lines, in addition to workstations, to achieve optimal results. A combination of linear programming and integer one is proposed to solve the optimization model of HDLBP that is composed of linear and U-shaped disassembly lines, with the objective of maximal disassembly profit. The feasibility of the model is verified by commercial solver CPLEX in solving different size problem instances. Note to Practitioners—This work deals with issue of using human workers only or using robots alone in disassembly lines and the limitation of each type of disassembly line layout. Most of the existing disassembly operation assignment methods are based on the correlation between humans and robots and the factors that affect disassembly. This paper suggests that the selection of humans and robots based on an optimization model that can be solved CPLEX. To leverage the unique advantages offered by each type of disassembly layout, this paper suggests the use of hybrid disassembly lines. Based on the idea of mixed integer programming, a hybrid disassembly line model of human-robot collaboration is designed and solved by CPLEX. The experimental results show that the hybrid disassembly line of human-robot collaboration has obvious advantages over the disassembly line composed of worker-only or robot-only when disassembling products. In the future research, we will use reinforcement learning algorithm to solve the hybrid disassembly line balancing problem, and consider more details of the human-robot cooperative hybrid disassembly lines.

Purpose: The integration of digitalization into the apparel manufacturing sector has become a strategic imperative, providing companies with a unique competitive edge. This study explores the application of ITEX PMD, an Internet of Things (IoT) data collection device, and evaluates the ITEX digital line balancing program within the context of digital lean management. By leveraging real-time data tracking, big data utilization, and advanced data analysis techniques, companies aim to enhance flexibility, agility, and operational efficiency in their manufacturing processes. Design/methodology/approach: This research delves into operational definitions for men's hoody sweatshirts, detailing the types of machines used, specifying standard task times, and elucidating the precedence relations of operations. The study utilizes ITEX PMD as a real-time production data collection device, generating periodic line reports through the associated software program. Findings: The ITEX digital line balancing program shows promise in assisting shop floor managers to optimize sewing lines with efficiency and seamlessness, contributing to overall operational excellence. The program provides another layer of visibility into the factory, reducing non-value-added activities, and improving efficiency. The ITEX Soft algorithm facilitates an ideal assembly line layout, ensuring a balanced workload among workstations, aligned with the continuous flow principle in the sewing line. Originality/value: This shop floor and software solution not only enhance visibility but also offer a practical means to reduce non-value-added activities, thereby improving overall efficiency. The ITEX Soft algorithm emerges as a valuable tool, contributing to a more balanced workload among workstations and optimizing assembly line layout. This study sheds light on the potential of digital lean principles in reshaping manufacturing processes and fostering operational excellence in the apparel industry.

This study investigates the transformative potential of integrating digital technologies with lean manufacturing principles to address the evolving challenges in the garment industry. The industry demands high-quality products, rapid delivery, cost efficiency, and production flexibility to maintain competitiveness and sustainability. While traditional lean methods have focused on waste reduction and process optimization, the advent of digitalization offers unprecedented opportunities for enhancing operational excellence. This research specifically examines the role of Internet of Things-based Process Monitoring Devices (PMDs) and digital line balancing in achieving these goals. PMDs, integrated within a digital lean framework, facilitate continuous monitoring of machine performance and operator efficiency, providing real-time insights into operational processes. This real-time visibility enables data-driven decision-making and centralized analysis, fostering agility and responsiveness. Furthermore, this study presents a dynamic digital line balancing algorithm that optimizes sewing line configurations and workload distribution. This dynamic approach allows for real-time adaptations in production flows to mitigate bottlenecks and boost productivity. By exploring the synergistic relationship between PMDs, digital line balancing, and lean principles, this paper contributes to a comprehensive understanding of how digital solutions can revolutionize operational efficiency in the garment manufacturing sector.

No abstract available

Energy efficiency has become a major concern for manufacturing systems, due to industry being the largest user of scarce, finite energy sources, and also to recent events which have pushed energy prices to alarming levels. In the present Industry 4.0 context, Reconfigurable Manufacturing Systems (RMS) are therefore one of the most promising manufacturing paradigm. In this paper, we investigate the suitability of one of the most common types of RMS, the Parallel-Serial manufacturing line with Crossover, to help minimise the peak of the electric power consumption. More specifically, the balancing of such a production line is studied, so as to integrate power peak minimisation from the design stage. Thus, we define the Parallel-Serial-with-Crossover Assembly Line Balancing Problem with Power Peak Minimization, a new combinatorial NP-hard problem. We also propose a suitable time-indexed Integer Linear Program that integrates balancing and scheduling decisions and a matheuristic algorithm designed to tackle large-size instances. Both approaches are tested on a wide set of instances. The computational results show that relevant power peak reductions can be achieved (33% on average), opening up promising perspectives from both algorithmic and managerial viewpoints.

Due to various interference factors, a pre-planned assembly scheme and its cycle time can be disturbed, resulting in the failure of product delivery on schedule. However, when assembly data of the production line can be obtained in real time, the balance of the assembly line could be dynamically adjusted in case of experiencing serious interferences to optimize its cycle time in time and improve its production efficiency. Therefore, this paper proposes a dynamic rebalancing framework by integrating real time manufacturing data into a novel serial two-stage adaptive alternate genetic fireworks algorithm for solving a stochastic type-II simple assembly line balancing problem (SSALBP-II). MES (manufacturing execution system) is used to obtain some real time data such as the resource status, operation information and task information and to judge abnormal phenomenon and the overdue delivery caused by interferences. On this basis, the stochastic type-II simple assembly line balance model is constructed, with a new serial two-stage adaptive alternate genetic fireworks algorithm (STAGFA). This new algorithm can incorporate both genetic algorithm and fireworks algorithm to solve the model according to the transformation of population diversity discrimination index. Through the comparison between STAGFA and other algorithms such as fireworks algorithm, genetic algorithm, other improved intelligent algorithms, it is proved that the STAGFA is effective and superior in solving the assembly line (re)balance problem. Then, the rebalanced scheme verified by simulation is dispatched to control the physical assembly line and realize dynamic rebalancing cycle time effectively.

Production line balancing is a crucial concept in the manufacturing industry, which involves the rational allocation of work tasks and resources to improve production efficiency, reduce costs, and ensure product quality. This article will delve into the core concepts, methods, practical applications, and challenges of line balancing. We will also discuss the use cases of line balancing in different industries and look ahead to possible future trends. Finally, through comprehensive discussion, readers will be provided with a deeper understanding and inspiration.

This study examined the application of Lean manufacturing tools and a novel approach called line balance loss analysis in optimizing production processes at a Malaysian automotive exhaust manufacturing company. The primary objective was to align production rates with customer demand. Data collection involved two key aspects: gathering production process data for catalytic converters, front pipes, and muffler subassemblies, and acquiring technical data on Lean tools, including the innovative line balance loss analysis method, from one muffler production line. The integration of Lean tools with the new line balance loss analysis approach was found to be crucial. The multi-process/multi-machine line balancing approach went beyond eliminating Non Value Added (NVA) activities and focused on determining manpower requirements and task allocation among operators. The integration of the new line balance loss analysis method improved task distribution across workstations, enhancing overall process efficiency. The study's analysis of Lean tool applications and the innovative line balance loss analysis provided insights into the cellular manufacturing system of the production line. These findings offered valuable information for management decision-making and process improvement, ultimately leading to increased productivity and cost savings for the company.

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For organizations looking to increase productivity and efficiency in the manufacturing sector, reducing Work In Process (WIP) is a crucial goal. The deployment of line-balancing techniques to accomplish this goal is the main topic of this research article. The introduction discusses the negative consequences of WIP, including productivity loss, leads times that are too long, and bottlenecks. The application of lean manufacturing principles, workflow optimization, WIP limits establishment, improved communication and collaboration, prioritization and sequencing of work, identification and removal of bottlenecks, and performance monitoring are just a few of the methods for reducing WIP that are covered. The importance of line balancing in maximizing efficiency, effectiveness, and productivity in manufacturing output is emphasized in the study. The literature review explores the use of worksharing and line-balancing approaches to increase productivity as well as the advantages of line-balancing in eliminating operational inefficiencies. The procedure for data collection and analysis, which includes calculating cycle time and takt time, is described in the methodology section. To reduce WIP and streamline processes, the suggested methodology advises allocating greater resources to bottleneck stations. The outcomes show how the suggested methodology works to decrease WIP and boost productivity. The paper concludes by comparing the advantages of line balancing in reducing workstations, cycle time, balance delay, idle time, and overall line length. Present. The references provided offer relevant studies on line balancing and assembly line basics. This research contributes valuable insights and practical implications for improving productivity and reducing WIP in the manufacturing industry.

Line balancing using the Takt Time method is recognized as a modern tool forassisting businesses in reducing costs and increasing profits. It is possible for the enterpriseto smoothly produce the most optimal number of products by rational calculation andresource arrangement. This paper presents a case study on the application of line balancingby the Takt Time method in a steel factory in Vietnam. The methods selected for the studyinclude describing the current situation of the factory, applying Lean Manufacturingimprovement tools, applying modern equipment technology, and rearranging factoryresources, finally calculating Line Balancing metrics and implementing them in practice.According to the findings of the research, using the Takt Time line balancing methodprovides numerous benefits to the factory. The number of operators decreased by 9 people.The percentage of line balance increased by 22% from 77% to 99% and the efficiency ofline balance increased by 6% from 93% to 99%. In addition, the study also helpsVietnamese businesses have a more objective view of the application of Takt Time linebalancing in the future.

This article presents applied research on line balancing within the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process, by Lean Methodology for garment modernization. It explores the application of line balancing in the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process. It aligns with Lean Methodology principles for garment modernization. Without the implementation of line balancing technology, the garment manufacturing process using hanger systems cannot improve output rates. The case study demonstrates that implementing intelligent line balancing in a straightforward practical setup facilitates lean practices combined with a digitalization system and automaton. This approach illustrates how to enhance output and reduce accumulated work in progress.

This study investigates the application of the Rank Positional Weight (RPW) method and line balancing techniques to improve Overall Equipment Effectiveness (OEE) and productivity on the machining line of an Indonesian automotive manufacturer. The automotive industry in Indonesia, particularly in the wake of the 2024 economic uncertainty, faces significant challenges such as reduced consumer purchasing power, rising vehicle prices, and disruptions in the global supply chain. PT XYZ, an automotive company, has faced inefficiencies in its production process, notably in its machining line, leading to high cycle times and low productivity. By implementing RPW and line balancing, the company aimed to optimize task distribution among operators and reduce production delays. This qualitative study employs a literature review methodology to examine existing research on RPW, line balancing, and OEE improvement in manufacturing systems. The findings demonstrate that RPW and line balancing significantly enhanced PT XYZ’s OEE from 75% to 89%, thereby increasing productivity and reducing operational inefficiencies. These results align with previous studies showing the effectiveness of these methods in improving manufacturing performance. The study concludes that RPW and line balancing are highly effective techniques for optimizing automotive manufacturing processes, offering practical solutions to address current challenges in the industry.

This study discusses line balancing in the production of irons at PT Philips Industries Batam, aiming to improve efficiency by addressing bottlenecks in the manufacturing process. The main focus is to optimize task distribution across workstations to minimize non-value-added time and enhance production line performance. Before the improvements, line efficiency was recorded at 94%, but there was still considerable idle time. Data collection was conducted through direct observation and time measurement of 18 operations using the Ranked Positional Weight (RPW) method. The analysis revealed that the implementation of Unique Device Identifier (UDI) labels led to increased operational times, particularly in operations 11, 15, and 18, causing workflow delays. After applying line balancing strategies, efficiency significantly improved from 67.97% to 96.23%. This study demonstrates that better task distribution can reduce production delays and support the achievement of operational targets. Key improvements include a reduction in balance delay and a smoother production flow, resulting in increased overall productivity. These findings highlight the importance of line balancing in optimizing manufacturing performance and provide valuable insights for industries seeking to enhance competitiveness through a more efficient production system.

Bangladesh is one of the world’s leading garment producers, with the apparel sector contributing over 80% of the country’s total export earnings and around 11% to its GDP. Therefore, guaranteeing competitiveness in the apparel manufacturing sector depends critically on the efficiency of garment production lines. One of Bangladesh's biggest garment companies, XYZ Group, has a denim jacket assembly line whose production efficiency is under improvement. In this company, 27 workstations on the current production line, efficiency is just 51.05%, suggesting unequal allocation of the workload. The objective of this research is to increase the line efficiency through three-line balancing approaches: ranked positional weight (RPW), largest candidate rule (LCR), and computer method of sequencing operations for assembly line (COMSOAL) which were used to redistribute work while preserving precedent requirements in order to solve these inefficiencies. With a balancing delay of 8.4% and a smoothness index of 72.11, COMSOAL achieved the highest efficiency at 91.6%, reducing the number of workstations to 15. Meanwhile, LCR and RPW also improved efficiency to 81.1% and 72.5%, respectively. This study offers a structure for raising denim jacket manufacturing production efficiency and can be modified to various clothing production lines to best use available resources and operational effectiveness.

Increasing competition and customized demands have led companies to use assembly lines more flexible and efficiently. Companies need to frequently rebalance their lines to adapt changes either in the product model demand or task processing times. During which, some tasks will be required to assign a different workstation (causing a change in the task allocation) due to the nature of the rebalancing procedure. However, as the number of relocations made during rebalancing increases, the likelihood of costs and quality errors will also arise. This study aims to efficiently balance mixed-model assembly lines while restricting the number of relocations to a limited value. A mixed-integer program is proposed to maximize line efficiency (minimising both cycle time and number of workstations, called type-E) considering the number of task relocations up to a certain value. An iterative algorithm is also developed for solving large-sized problems. The model allows lower and upper bounds to be imposed on the cycle time and aims to avoid ‘substantial’ changes in task assignments during rebalancing. The multi-manned workstation case is also integrated, which gives the advantage of determining the number of operators, i.e., increasing line efficiency. Tests have shown that the heuristic algorithm achieves competitive solutions in compare with the mixed-integer programming model within short periods of time, including large-size problems.

With the diversification of market demands and the limited availability of production resources, optimizing the allocation of manufacturing tasks within the constraints of limited workstation resources becomes increasingly important. This study explores the workstation buffer zone disassembly-assembly line balancing problem, aiming to improve the operational area of workstations and reduce component transportation costs. Based on the characteristics of the problem, a computational model is developed to maximize the recovery profit. To facilitate the search for an optimal solution, the parallel advantage actor-critic (PA2C) algorithm is used to address this problem, and the feasibility of the developed approach in disassembly and assembly lines is analyzed. Comparisons with AC and the original A2C suggest the competitive performance of the proposed solution.

The disassembly and assembly line balancing problem (DALP) is a critical task in industrial production, involving the efficient organization of disassembly and assembly tasks to improve the productivity and flexibility of production lines. In practical applications, task allocation, robot movement, and workstation layout optimization are key factors affecting production efficiency. This study proposes an improved parallel advantage actor-critic algorithm to address DALP with space constraints due to robot movement. Considering the limitations of workstation space, this approach optimizes the robot's movement paths between workstations, reducing the cost of opening workstations, and optimizing task allocation strategies. To enhance the convergence speed and stability of the conventional Parallel A2C algorithm, action space optimization and a greedy strategy are incorporated into the algorithm. Experimental results demonstrate that the improved parallel advantage actor-critic outperforms the A2C and AC algorithms in terms of efficiency and performance, particularly in handling disassembly tasks with space constraints, significantly improving the operational efficiency and economic benefits of the production line.

As technologies advance rapidly, increasing the manufacturing workstation efficiency is essential for boosting productivity and cutting costs. To address it, researchers and practitioners usually focus on factors such as labor distribution, human-machine collaboration, or task scheduling, but with limited consideration in the utilization of workstation space. For manufacturing workstations with a specified size, the dimensions of the components determine the allocation of tasks. Effectively utilizing the idle space of workstations can significantly improve manufacturing productivity, reduce resource waste, and enhance the reliability and capacity of production lines. This work proposes an innovative scheme aiming at optimizing and facilitating manufacturing efficiency through the leverage of idle space in disassembly and assembly lines. An improved advantage actorcritic algorithm is proposed to positively solve this optimization problem by incorporating a greedy strategy. The greedy strategy enables the algorithm to converge more quickly, thereby reducing computation time and resource consumption. Comparative experiments and analysis with other optimization methods demonstrate the competitive performance of the proposed solution in both solution accuracy and efficiency.

The progress of science and technology speeds up the replacement of products and produces a large number of end-of-life products. Traditional incineration causes a waste of resources and pollution to the environment. Disassembling and recycling end-of-life products are the recommended way to maximize the utilization of resources and reduce environmental pollution. Disassembly performance is affected by many factors, such as the disassembly posture of the human body, the fatigue of workers on a workstation, disassembly profit, and task precedence relationship. In this article, a mixed integer linear programming mathematical model for U-shaped layout disassembly line balancing problems is developed, in which the balance of workers’ fatigue indices is an optimization objective in addition to disassembly profits. An efficient solution to the problem that uses a collaborative resource allocation strategy of the multiobjective evolutionary algorithm is proposed. The linear programming solver CPLEX is used to verify the accuracy of the model and compared with the proposed algorithm. Experiments demonstrate that the algorithm is significantly superior to the CPLEX solver in handling large-scale cases. The proposed algorithm is also compared with two well-known algorithms, which further verifies its superiority.

Abstract Under the pressure of climate change, energy-efficient manufacturing has attracted much attention. Robotic assembly lines are widely-used in automotive and electronic manufacturing. It is necessary to consider the energy saving and economic criteria simultaneously when robots are utilized to operate assembly tasks replacing human labor. This paper addresses an energy-efficient robotic assembly line balancing (EERALB) problem with the criteria to minimize both the cycle time and total energy consumption. We present a multi-objective mathematical model and propose a bound-guided hybrid estimation of distribution algorithm to solve the problem. When designing the optimization algorithm, we adopt estimation of distribution algorithm (EDA) to tackle the task assignment, and design a non-dominated robot allocation (NGRA) heuristic which is embedded into the EDA to allocate suitable robot to each workstation. Moreover, we propose a bound-guided sampling (BGS) method, which is able to reduce the search space of EDA and focus the search on the promising area. The computational complexity of the proposed algorithm is analyzed and the effectiveness of the proposed NGRA and BGS is tested. In addition, we compare the performances of the proposed mathematical model and the proposed algorithm with those of the existing model and algorithms on a set of widely-used benchmark instances. Comparative results demonstrate the effectiveness of the proposed model and algorithm.

The Stochastic Parallel Disassembly Line Balancing Problem (SP-DLBP) presents significant challenges in optimizing task allocation due to large-scale multi-objective optimization and complex production environments. This paper proposes a reinforcement learning-based method that integrates a MultiHead Attention CNN-DQN algorithm with an Equilibrium Monte Carlo Tree Initialization (EMCI) approach. The method includes a CNN-based feature extraction module for modeling dynamic task distributions and a Multi-Head Attention mechanism to capture task dependencies for improved global optimization. The EMCI method optimizes the exploration strategy, enhancing solution quality and convergence speed. Experimental results on public datasets and real-world applications, such as refrigerator disassembly, demonstrate significant improvements in workstation count, load balancing, and solution stability compared to traditional algorithms. The proposed method proves effective in solving high-dimensional, coupled disassembly tasks and offers valuable insights for reinforcement learning applications in complex environments.

Assembly line balancing is a critical aspect of modern manufacturing, aiming to enhance productivity by minimizing bottlenecks, idle time, and inefficient resource allocation. This paper focuses on optimizing a drone assembly process using the Largest Candidate Rule (LCR) heuristic, a method not widely demonstrated in this application domain. The research models a baseline assembly line where each distinct task is assigned to an individual workstation, then systematically applies the LCR to reallocate tasks while respecting precedence constraints. Through simulation of an 8-hour shift with fixed demand, the LCR-based configuration reduces the number of workstations from 11 to 8 and increases the minimum station efficiency from approximately 30% to 67%. Downtime percentages are also significantly lowered, leading to improved synchronization and resource utilization. However, the overall throughput remains constrained by bottleneck task cycles, highlighting the need for future work targeting key task redesign. These results illustrate both the effectiveness of heuristic line balancing and its limits, emphasizing the measurable gains in cost-effectiveness and production efficiency when intelligent task assignment is combined with targeted process improvements.

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Human-robot collaboration is increasingly utilised in assembly lines, where task allocation is critical. To address the task allocation problem, this paper first evaluates each assembly task using the indicator of automation potential to determine if a collaborative robot can complete it. The method for evaluating assembly complexity and workload is then introduced, which determines the assembly complexity of each task for both robots and workers, as well as the workload for workers. Based on the above indicators, a new task allocation optimisation model for the human-robot collaborative assembly line is established with the objectives of minimising the cycle time, minimising the workload variance between different workstations, and the assembly complexity per unit product. An improved multi-objective migratory bird optimisation algorithm with fast non-dominated sorting is developed to solve the mathematical model of this task allocation. Finally, the proposed method is applied to an assembly line in a real enterprise. The results of algorithm comparisons show that the proposed algorithm is effective, and some managerial insights are also derived from the experimental tests. The result shows that the study effectively reduces product assembly complexity and balances workers’ workload across stations while maintaining assembly efficiency.

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ABSTRACT Human-robot collaboration (HRC), as a part of Industry 4.0 strategy, requires a completely new type of robots able to co-work with humans, called collaborative robots or cobots. This kind of collaboration is especially needed in assembly systems, which are known for having a low level of automation. For some assembly tasks human is still an irreplaceable factor. On the other hand, some assembly tasks are monotonous and tiring for humans. Therefore, the different approaches to cope with the challenge of identification and selection of proper task allocation between human worker and cobots are reported by many researchers. It is not an easy task since multiple and often conflicting criteria need to be taken into account. Some kind of artificial decision support is needed, to successfully solve this problem. In this research, task allocation procedure is presented for identification of different improvement options that utilize cobots into the assembly line for different tasks to be performed. The decision support system based on the HUMANT algorithm has been used for selection of the option which represents the best compromise solution. The procedure is experimentally tested on the assembly line with car gearboxes as a real product.

There are successful cases in lean manual assembly lines; however, in some cases, such as the ease of assembly in quicker cycle time, the designs are not satisfactory and must be transformed to semi-automation. This research studies human-robot task allocation when designing for semi-automation considering not only time-cost effectiveness as in the existing research but also assembly difficulty and ergonomic issues. A proposed methodology optimally determines what tasks should be performed by humans or robots, at which station, and in what sequence. A multi-objective linear programming (MOLP) model is proposed to simultaneously minimize total operating cost, cycle time, and ergonomic difficulty. Solving the model has two approaches: with and without optimal weights. The methodology is applied to a Lego-car assembly line. To illustrate the benefits of the proposed MOLP, a comparison between it and three single-objective models is made. Results show that the optimal-weight MOLP yields a better performance (a shorter cycle time, a lower cost, and especially, a significant ergonomic improvement) when compared to the other MOLP and single-objective models.

Digital assistive systems, enable workers with disabilities to perform complex industrial work. However, the previously presented systems considered only a single workplace and a single user. This paper presents an assembly line that enables a joint processing of complex tasks by multiple workers with and without disabilities. The aim was to investigate the use of interaction technologies such as in-situ projections and hand-tracking to enable the processing of complex assembly tasks by work teams with highly heterogeneous abilities. The developed assembly line assists users and coordinates the joint work by distributing single assembly steps to workers based on the individual workers' abilities. Besides presenting the concept and implementation of the assembly line, we report our findings after six months of operation. Our results indicate that using the assistive assembly line has positive impacts, such as increased satisfaction and independence of the workers combined with a higher productivity.

Industry 4.0 proposes the integration of artificial intelligence (AI) into manufacturing and other industries to create smart collaborative systems which enhance efficiency. The aim of this paper is to develop a flexible and adaptive framework to generate optimal plans for collaborative robots and human workers to replace rigid, hard-coded production line plans in industrial scenarios. This will be achieved by integrating the Planning Domain Definition Language (PDDL), Partial Order Planning Forwards (POPF) task planner, and a task allocation algorithm. The task allocation algorithm proposed in this paper generates a cost function for general actions in the industrial scenario, such as PICK, PLACE, and MOVE, by considering practical factors such as feasibility, reachability, safety, and cooperation level for both robots and human agents. The actions and costs will then be translated into a language understandable by the planning system using PDDL and fed into POPF solver to generate an optimal action plan. In the end, experiments are conducted where assembly tasks are executed by a collaborative system with two manipulators and a human worker to test the feasibility of the theory proposed in this paper.

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This paper presents the development of a stochastic cost model aimed at determining the optimal number of operators required for a workstation, considering the activities performed by them, the time required for each activity, and the associated costs. The model takes into account the inherent variability in task performance and the corresponding costs involved. By utilizing the derived time equations, the study identifies both value-added and non-value-added activities, along with their respective costs. The proposed optimization model not only enables the estimation of the required number of operators but also provides insights into the impact of addition of workstation on the overall cost of the product. By considering the variability in task performance and associated costs, the model offers a comprehensive approach to operator allocation and workstation planning. Through the application of this stochastic cost model, organizations can make informed decisions regarding operator allocation, optimizing the utilization of available resources while minimizing costs. The findings of this research contribute to the understanding of the relationship between operator allocation, number of workstation, and production costs. The developed model serves as a valuable tool for decision-makers in the manufacturing industry, enabling them to analyze and optimize their operations effectively.

Aiming at the inefficiency caused by an unbalanced workstation load in the mixed-model assembly line (MMAL), we study the assembly line (AL) design and load balancing problem under parallel tasks. Considering the task configuration cost, workstation opening cost and penalty cost of unbalanced load on the assembly line, a mixed integer programming model with the workstation’s space capacity constraint is established to formulate the mixed-model assembly line load balancing problem (MMALLBP), which is aiming at minimizing the total cost. In addition, the simulated annealing algorithm with an improvement strategy is proposed. Numerical experiments using the improved simulated annealing algorithm are superior to the solver in terms of solving time and stability, and the solving accuracy is higher than that of the traditional simulated annealing algorithm. Allowing parallel tasks can flexibly allocate tasks to the workstations, effectively use the idle time of the workstations, reduce the number of opened workstations, improve the production efficiency, reduce construction costs and the risk caused by the unbalanced load of AL.

The pursuit of assembly line balance aims to enhance efficiency by optimizing the ratio between output and input. Achieving balance necessitates meticulous planning to ensure that machines at each workstation operate with equitable workloads. Notably, the assembly line plays a crucial role in this equilibrium. In a manufacturing company, excels in timely product delivery to customers. However, a decline in productivity is attributed to inefficient production processes. The production department operates based on takt time, aligning with customer demand requirements. Despite meeting customer demands promptly, the company usually needs to work on productivity due to fluctuating customer demands and varying process capacities. The consistent use of a capacity production leads to stable productivity and high loss of time due to efficient work hours. Driven by the background, the researcher aims to delve into workstation arrangements for efficient production processes, with the ultimate goal of minimizing wait times, analyzing work hour precision to reduce loss time, and optimizing time usage based on takt time in the production line for efficiency. The study incorporates line-balancing methods to enhance time usage effectiveness in each production line.

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In turbulent times featuring increased customisation, higher demand volatility, and shortened product life cycles, companies gain a competitive advantage by adopting a single yet highly flexible assembly line. A cornerstone of today's production systems is determining the optimal takt time (or cycle time) by aligning the assembly pace with the desired level of output. In practice, most companies rely on a fixed takt time, even when the work content between models varies considerably. We show that, in contrast to a fixed takt system, variable takt times reduce not only labour inefficiencies but also the complexity of the mixed-model assembly line balancing problem. Inspired by our industry partner Fendt, an innovation leader in the global agricultural machinery market and benchmark for a wide range of industries, we define a generalisable mixed-integer programming model that accounts for key conditions neglected in previous research – in particular, random customisation through configuration-specific task times and assembly quality by assigning operator workloads to ‘zones’. Introducing such operator work zones reveals that firms need not face a time–quality trade-off whereby takt time must be prioritised over how well work is performed. Our numerical study and takt time sensitivity analysis document the effectiveness of this approach when its results are compared with those under Fendt's current takt times.

This study is aimed to help the company doing an assembly line balancing to minimize station time as not to exceed takt time and minimize idle time between workstation, using Mixed Integer Programming (MIP) method with Multi-manned Assembly Line Balancing (MmALBP) approach using two mathematical model, which 1st model aimed to minimize the cycle time of all workstation, then those cycle time being a parameter in 2nd Mathematical model to specified the optimal number of workers also balancing workload between operators. Result from this research were able to minimize station time from 218.56 hour to 166.3 hour so it didn’t exceed the takt time and also could reduce total idle time until 45% from 905.2 hour in actual condition become 499.49 hour with decreased number of operator from 52 to 22 person, and increase the efficiency of assembly line from 54% in previous to 67% in the proposed line.

To solve the problem of insufficient utilization of equipment and low production efficiency of the fuselage production line of certain aircraft program, this article analyzes and studies the fuselage assembly process and assembly station cycle, establishes the geometric model and logic simulation model for the production line by means of the discrete event simulation software Plant simulation to simulate the assembly of large components and find out the bottleneck restricting production capacity increase in the assembly process. The simulation results have been applied to optimize the takt time and operation process of fuselage assembly line, with a set of production line improvement plan generated, which realizes the resource balance and capacity increase of the production line.

An important and highly complex process in the automotive industry is the balancing of the assembly lines. Optimally distributing jobs among the lines in order to obtain the highest efficiency is mostly done manually, taking a lot of time. This paper aims to automate the process of line balancing for a real-world test case. Automotive assembly lines are highly complex, and multiple factors have to be considered while balancing the lines. All factors relevant in a case study at VDL Nedcar are considered, namely, mixed-model production, sequence-dependent setup times, variable workplaces with multiple operators and multiple assignment constraints. A Genetic Algorithm (GA) is proposed to solve the formulated balancing problem and to act as a decision support system. Results on newly proposed benchmark instances show that the solution is dependent on the relation between the takt time and processing time of jobs, as well as the setup times. In addition, results of a real-life case study show that the proposed GA is effective in balancing a real-world assembly line and that it can both increase the efficiency of the line and decrease the variance in operating time between all model variants when compared to current practice.

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Industry trends such as product customization, radical innovation, and local production accelerate the adoption of mixed‐model assembly lines (MMALs) that can cope with a widening gap between model processing times and true build to order capabilitiy. The existing high work content deviations on such assembly lines stress production planning, especially the assembly line sequencing. Most manufacturers set the launching rate for all assembly line products to a fixed launching rate resulting in rising utility work and idle time when system load increases. We present an “ideal” variable rate launching (VRL) case resulting in minimal computation and achieving 100% productivity (full elimination of idle time and utility work) for balanced assembly times and homogeneous station lengths. Managers should foster the ideal circumstances where operators need not wait for a preceding task to be completed and product sequence restrictions are eliminated, thus enabling unmatched production flexibility. Furthermore, we present a mixed‐integer model to analyze both closed and open workstations on an MMAL for fixed rate launching and VRL. This model incorporates costs not only for labor inefficiencies but also for extending the line length. We present a heuristic solution method when process times and station lengths are heterogeneous and demonstrate that the variable takt dominates the fixed takt. In a numerical, industrial benchmark study, we illustrate that a VRL strategy with open stations has significantly lower labor costs as well as a substantially reduced total line length and thus lower throughput time.

Natural disasters, pandemics, and political nationalism force companies toward more responsive, flexible, and resilient assembly systems. For manufacturers, adaptability of the assembly process and local production ensure short product lead times even during supply chain disruptions. Yet one downside of regional production is that fixed takt time assembly lines become overburdened, especially when customisation is unlimited. In this context, variable takt time groups (VTGs) are a major competitive lever. We introduce the notion of a workload equilibrium balancing overload and underutilization. This preliminary stage of the assembly line balancing and sequencing problem significantly reduces the planning effort. Moreover, we present a model for minimising (i) the number of VTGs for a given maximum operator drift per unit or (ii) the maximum operator drift per unit for a given number of VTGs. We solve these dynamic problems by developing a heuristic approach: the variable takt time groups algorithm (VTGA). In our analysis of three real-world data sets from two German manufacturers—Fendt and Rolls-Royce Power Systems—we benchmark the VTGA against existing takt times. We find that VTGs result in higher labour efficiency than a fixed takt time and that the VTGs segmentation level plays an important role in reducing operator inefficiencies.

. The primary objective of the research was to shorten the cycle time of a particular process used in producing a new Wavelength Selective Switch (WSS) product by a multinational electronic manufacturing corporation. In recent years, the case study company has encountered difficulties with process cycle time exceeding predefined takt time when establishing a new production process for the freshly launched item. To identify areas for improvement, the study leveraged industrial engineering techniques, such as the Yamazumi Chart, line balancing (workload leveling) analysis, and method time measurement. After the production process data was thoroughly analysed, cycle time reduction opportunities emerged. After that, the jig design used in the present investigation was developed based on the highly effective and widely recognized mechanical engineering concepts of Design for Assembly (DFA) and Design for Disassembly (DFD). The aim was to confidently eliminate non-value-added processes in the fiber ribbon orientation step, resulting in increased efficiency and improved outcomes. The study reported a significant reduction of 87% in the cycle time required. The results also demonstrated that implementing certain methodologies could reduce the cycle time. In addition, this finding held significant importance for the industry, as it could lead to increased efficiency and productivity, ultimately leading to cost savings of 12% of its total production.

Although the workstations of a Brazilian automotive electrical harness production line are set close to TAKT time (the production rate required to meet demand), factory performance is compromised regarding: (i) sick leaves due to occupational disease (105 employees last year) and (ii) a production rate at only 42% of capacity. Our objective was to simulate the performance of a production line balanced against physical overload by the addition of an extra workstation. Based on ergonomic work analysis, the study applied System Dynamics at the global observation stage to obtain a systemic interpretation of the factors involved in production line performance. According to the indicators, the alternative configuration reduced physical overload by 36%, which would result in a sick leave rate of 50.8 employees/year (51.6% lower than the current configuration), as well as a production rate at 99% of capacity (a 92.7% increase over the current configuration). We found that reducing physical overload allows the "workforce control" loop to govern the system, producing favorable results. We conclude that setting the work cycle overly close to TAKT time leads to overload, due to the shorter recovery times at the end of each cycle. Thus, it is necessary to seek a balance between efficiency gains through downtime reduction and the physiological recovery of workers.

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In this competitive era, there are no more manufacturing organizations that want to produce products in large volumes with low variations. The company is now shifting toward the idea of producing more variations that are of interest to consumers. Increased demand and production targets cannot be avoided. however, the increase must be adjusted to the worker’s resources according to their function. If it is not balanced, it will cause a bottleneck on the production flow caused by the operating time at a workstation exceeding the takt time. then we need to balance the assembly line to get optimum and efficient results. Assembly line balancing (ALB) is used to flatten workload on all processes in a cell or value stream to eliminate bottlenecks and excess capacity, with a limitation of slowing down process time and excess capacity. The completion of this case study uses a combination of Priority Rule- Based/heuristic methods and Genetic Algorithm methods.

In last decades, competition between several industries to deliver high quality products is marginally increasing in terms of costs and performance. Lean manufacturing is one of those activities, which focus on the reduction of cost by eliminating non value adding activities. The general idea behind Lean manufacturing is to eliminate non value adding activities which again leads to eliminate waste. Higher flexibility, better quality and effective production system the manufacturer requires new ideas on production line. All the Lean tools are used to implement newer ideas into production line which improves the overall production process. This Paper addresses the application of lean manufacturing philosophy to the compressor assembly line of a well-known industry. In this paper, Failure Mode Effective Analysis, Value Stream Analysis and Takt time are the lean tools effectively integrated resulting in the improvement in terms of cycle time, space available, distance travelled by worker and productivity which leads to delivering the customer demand within time.

The development of an industry that continues to move forward coupled with global competition and openness demands that the company continues to evolve and always make improvements in improving the performance of its production process. XYZ Corp is an automotive company based in Germany that produces premium cars, the car assembly process groove at XYZ Corp is a trimming line, mechanical line and finishing line, XYZ Corp produces A-model, B-model and C-model cars. At this time XYZ Corp not achieving the production target due to the car assembly cycle time on the trimming Line 1 area exceeds the specified takt time. Assembly line balancing is required in the trimming area using Mixed-Model Assembly Line Balancing Problem (MALBP) approach to minimize the number of workstation, in the Trimming area assembly line balancing study using the Ranked Positional Weighted with Moving Target (RPW-MVM) method. Alocation constraint should be added due to machine restrictions that cannot be moved. After assembly line balancing, there was a decrease in the number of workstations to 14 workstations with a line efficiency of 86% and balancing efficiency of 97%.

The design of a production system is a strategic level decision. One of the key problems to solve is the line balancing problem that determines the efficiency of a production or assembly line. This class of problem has been widely studied in the literature. It determines important features, such as the number of stations, the takt time or the working conditions. Most of the variants of this problem consider only one objective function, but nowadays companies have to take into account different criteria. In this study, we consider a bi-objective variant of the simple assembly line balancing problem. We present a generic branch-and-bound method to solve exactly this problem. The objective functions are to minimise the takt time and the number of stations. To do so, bounds and bound sets are developed. The resulting method is numerically tested and compared to an ϵ-constraint method. These experiments show that the bi-objective branch-and-bound algorithm outperforms an ϵ-constraint method using a state-of-the-art single objective algorithm for more than 80% of the instances. Finally, we propose an analysis of the cases where the branch-and-bound method is outperformed.

Unbalanced production rates of activities and abundant resource allocation are the leading reason behind bottlenecks in processes and have been one of the causes that negatively affect projects leading to wasted resources. Many industries suffer from unbalanced resource workloads, where manufacturing takt times at some workstations are out of sync with preceding stations, consequently leading to an abruption in the workflow between activities. This research aims to assess the current state of the manufacturing process of a wall assembly line from material cutting to installation, identifying bottlenecks, and creating a framework that would contrast both cycles to finally propose a solution through simulation. A case was studied to propose innovative methods to improve the process flow and to eliminate any waste generated by bottlenecks. This will not only reduce the process duration but will also significantly increase cost expenditure since the amount of idle time and resources will be reduced.

The lean approach is often used by manufacturing companies to increase productivity in the production area. In this case PT P, a manufacturing company that produces refrigerators, faces the same problem on the unloading process in the final assembly area. The unloading process is the operation to move the finished product from the production line and store it in the finished goods warehouse. Operators in the unloading area have tasks to unload finished products from conveyors which are connected directly from the assembly line and placing them on pallets. Due to the refrigerator is quite heavy a forklift has to use to move the full pallet to the finished goods warehouse which is located next to the unloading area. Once finished the forklift returns to the unloading area with the empty pallets and places them in the unloading area, to be refilled by the unloading operator. Based on the data taken, the cycle time in the unloading process was recorded to be varied and the highest was 33 seconds, exceeding the takt time that had been determined on the production line, which was 24 seconds. This means that there is waste that occurs in the unloading area. The long and unstable cycle time in the unloading process makes the production line disrupted because the conveyor often stops and causes productivity to drop. For this reason, it is necessary to analyze and improve the process with the Lean concept by reducing the existing waste, so that the cycle time becomes stable and smaller than the takt time.

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Manufacturing sector is regularly facing the challenges caused due to high market dynamics and mass customization. Traditionally designed machine cells fail to address this issue due to lack of flexibility in capacity and functionality. However, the benefits of cell based production can be achieved by changing the machines involved and the design of cells. This paper presents a hybrid model of machine cells comprising of reconfigurable machine tools (RMTs) that are acting as part feeders for a lean assembly line of discrete products. The strategies of lean manufacturing to maintain the Takt time and synchronized one-piece-flow are considered in the model. A multi-objective optimization problem is formulated and solved to minimize the inter-cellular part movement, the error for Takt time among machine cells and the total reconfiguration time of the RMTs using NSGA-II metaheuristic. A numerical case example for the model is solved using MATLAB© and illustrated along with computational steps and Pareto optimal solutions.

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ABSTRACT This paper presents a manufacturing model for integrating reconfigurable machine cells as feeders of components to a multi-product assembly line exhibiting a prominent lean characteristic; synchronisation with Takt time. Two mathematical formulations in the form of combinatorial optimisation problems are developed to identify the best reconfiguration for the machines by optimum selection of the modules in the cells to minimise the total error with Takt time and then the optimum sequence of assembling the products to minimise the total reconfiguration time and effort. An elitist genetic algorithm (GA) meta-heuristic is employed twice in succession to search the optimal solutions for both the minimisation problems. The best machine configuration found from the first search is utilised further to identify the optimal assembly sequence. A hypothetical data set representing the features and parameters of the model in conformance with any typical mixed-model assembly is presented for illustration of computational procedure. Synchronisation among machine cells and tardiness are considered as performance evaluation measures to validate the effectiveness of the proposed model.

On the basis of the previous research, the organization design of the assembly line is carried out. With constraint conditions, according to the standard time and the considered wide time, assembly line takt time is calculated, and found the bottleneck process with the longest processing time, designed for the assembly line load. According to the process capacity and process rate, calculated each process needs to be completed in the system time of processing task, time quota, interval number, and work time, and drew out the standard work instruction chart. Through hours restructured, station merged, to reduce the bottleneck process and intermittent time, improve the assembly line load, finally the assembly line workplaces are redistributed, and the workers are arranged reasonably, and then the organization structure design of the assembly line is carried out, a production line is formed for the enterprise.

A good manufacturing practice for production assembly can result in better productivity for the organization. This study focusses on productivity improvement of Electronics Manufacturing Service Company (EMSC) production assembly. It also aims to reduce waste and to propose a leaner line balancing with the elimination of non-value-added activities during the assembly. This study also aims to utilize current workstation to carter for the needs of the workstation to achieve 39 seconds takt time. Time study has been conducted for all workstations and found that all of the cycle time for each workstation exceeds the required takt time of 39 seconds. The bottleneck is at workstation number two with a recorded standard time of 54.1 seconds to complete the assembly thus causes high work in progress at workstation number two and excessive operator idle time to subsequent workstations. The main issue of EMSC is high volume demands from the customer and committing to the shipment plan. By adding one additional operator to workstation number two and a proper distribution of tasks may help EMSC to increase their production output. Time study is recorded in standard work form for future improvement.

In a reality of global competition, companies have to minimize production costs and increase productivity in order to boost competitiveness. Facility layout design is one of the most important and frequently used efficiency improvement methods for reducing operational costs in a significant manner. Facility layout design deals with optimum location of facilities (workstation, machine, etc.) on the shop floor and optimum material flow between these objects. In this article, the objectives and procedure of layout design along with the calculation method for layout optimization are all introduced. The study is practice-oriented because the described case study shows how the layout of an assembly plant can be modified to form an ideal re-layout. The research is novel and innovative because the facility layout design and 4 lean methods (takt-time design, line balance, cellular design and one-piece flow) are all combined in order to improve efficiency more significantly, reduce costs and improve more key performance indicators. From the case study it can be concluded that the layout redesign and lean methods resulted in significant reduction of the following seven indicators: amount of total workflow, material handling cost, total travel distance of goods, space used for assembly, number of workers, labor cost of workers and the number of Kanban stops.

One of the most important ways to achieve a competitive advantage in the market is to meet customer needs. Nowadays competition is more significant than ever before so it is necessary to establish a safe and reliable relationship with customers and thus gain customer loyalty. One of the most effective lean tools which gives the possibility to see the entire process in a somewhat simplified way and can be the initiator of many further changes is value stream mapping (VSM). Based on the collected data during the VSM walk, a visual review of the process was made and takt time was calculated. In this study, it was necessary to balance the workplaces, so that they are all approximately loaded, and this resulted in a reduced cycle time on the assembly line and many other additional benefits presented in the paper. This study also covers how the application of VSM improves planning flexibility in the automotive manufacturing process.

ABSTRACT Despite the increasing degree of automation in industry, manual or semi-automated are commonly and inevitable for complex assembly tasks. The transformation to smart processes in manufacturing leads to a higher deployment of data-driven approaches to support the worker. Upcoming technologies in this context are oftentimes based on the gesture-recognition, − monitoring or – control. This contribution systematically reviews gesture or motion capturing technologies and the utilization of gesture data in the ergonomic assessment, gesture-based robot control strategies as well as the identification of COVID-19 symptoms. Subsequently, two applications are presented in detail. First, a holistic human-centric optimization method for line-balancing using a novel indicator – ErgoTakt – derived by motion capturing. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and the takt-time balancing. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-score and the cycle time of each assembly workstation with respect to the workers’ ability. The second application is gesture-based robot-control. A cloud-based approach utilizing a generally accessible hand-tracking model embedded in a low-code IoT programming environment is shown.

Feedback control systems utilised in car body construction cause process time variance when compensating for external disturbances. By considering these in robotic assembly line balancing, the risk of cycle time violations can be controlled. This requires knowledge of the underlying process time distributions, which are not known in advance. Therefore, a simulation method is proposed to assess the impact of varying process time distributions on the balancing of robotic assembly lines. The initial step involves acquiring the process times of existing production processes. In the subsequent simulation, these are randomly and repeatedly selected as substitutes for the process times in the balancing of a new robotic assembly line. The impact of process time distribution variations on the result is investigated, and a single solution can be selected. The proposed method is evaluated based on the balancing of a robotic assembly line for a body-in-white rear compartment. Results are compared to normally distributed process times, which is a common assumption for modelling uncertain process times. Both approaches are evaluated utilising actual process time distributions. It is demonstrated that the proposed method yields fewer and less severe underestimations of cycle times, thereby reducing the number of uncontrolled violations of cycle times.

Human factors play an important role in the optimization of industrial processes. Factors such as a worker's skill, effort, condition, and consistency have a significant impact on his performance. The objective of this paper is to determine the effect of human factors on the efficiency of assembly lines and labor costs by using the Classical Westinghouse Method (CWM). Using a time study-based mathematical model, the efficiency of the assembly lines and labor costs are optimized by considering human factors such as workers' skill, effort, condition, and consistency. The model is validated by a time study of the serial manufacturing industry in Pakistan. In a nutshell, this model may enable the serial assembly line manufacturing industries to improve efficiency and optimize labor costs by taking human factors into account.

: The emergence of technologies linked to the Industry 4.0 paradigm is increasingly influencing the design and management of production systems. However, applications related to assembly lines are scarcely explored in the literature. Hence, in this paper, a Digital Twin-based approach to real-time assembly line balancing problem (ALBP) in the i-FAB learning factory of Università Carlo Cattaneo – LIUC is presented. The results show that the implementation of a Digital Twin (DT) can enhance the overall productivity of a manual assembly line to smooth the effects of disruptions.

Industrial washing and cleaning of the components is an integral part of the business with the refurbished parts. The cleaning quality of the refurbished parts must be at the high level. One such technique for cleaning the components is the method of wet blasting using the chemicals at a certain temperature. Such process can be balanced by changes in the concentration of the chemical, the abrasive used and the temperature, resulting in time reduction of the cleaning cycle. It is especially important if the process is also a bottleneck.

Many problems occur when assigning tasks to work centres, especially in determining the required number of workstations for line balancing which requires a minimum theoretical number of workstations. The most common problem is bottleneck. In this paper, a method is proposed to solve floating tasks problem in single-model line when the actual required number of workstations exceeds the minimum theoretical number, and the standard time of the floating task (work center) exceeds the cycle time. The floating task will represent a critical bottleneck activity in line. The proposed method depends on minimizing the standard time of critical bottleneck and non-critical activities by a minimum free-floating time depends on the average of slack times of the non-critical activities, and it will increase the line efficiency from (77%) to (88%), and balance delay is minimized from (23%) to (12%).

No abstract available

This research aims to identify and overcome bottlenecks in production process at PT. XYZ Indonesia, especially in HA Export Department on SMC Big Volume Lane. Bottlenecks were detected in Visual Acc and Housing processes, which resulted in decreased efficiency and productivity. The method used in this study is line balancing with the Pro Model simulation approach, which allows analysis of improvement scenarios without disrupting ongoing operations. Data collected includes process time and machine capacity. Initial simulation results showed significant idle time 15,4%, with accumulation at Raw Material workstation (12.30%), Housing workstation (1.20%), and Visual Acc (1.90%). After the improvements were made, including increasing the Raw Material capacity to 20 pcs, the Housing process to 3 pcs, and Visual Acc to 3 pcs, the bottleneck was successfully eliminated from 15.4% became 0% and the production flow became more stable. This research provides practical solutions to improve efficiency and reduce cycle time, and can be a reference for companies in implementing line balancing and simulation methods to improve productivity in the manufacturing industry.

No abstract available

Abstract Value Stream Mapping (VSM) is a widely used industrial tool to represent material and information flows, helping to identify improvement opportunities and define the desired future state. However, its construction often raises difficulties that can lead to mapping errors. This paper draws on the authors’ experience of more than three decades, in academic and industrial contexts, to systematise recurrent misunderstandings observed in VSM construction. The study focuses on six problem areas: (1) distinctions between value-added time, processing time, and cycle time; (2) process lead time and inventory lead time; (3) inventory quantification; (4) representation of multiple material flows; (5) treatment of shared processes; and (6) system balancing and bottleneck identification. Several of these issues are absent from the literature, while others, although mentioned, continue to be misapplied. For each, the paper provides clarification and/or a corrective approach, thereby contributing to a more rigorous and consistent use of VSM.

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- The assembly line in any manufacturing industry serves the utmost importance in the entire manufacturing system as it represents the final production of the factory floor. The rate of production of industry is governed by the cycle time at the bottleneck station. Therefore, the cycle time analysis of the assembly line using standard work measurement techniques is of utmost importance for assessing the productivity of the shopfloor. In order to address the ever-increasing demands of capacity, the systematic methodology for work measurement, process design and two-sided mixed-model assembly line balancing (TSMMALB) has been proposed. Initially, the analytical model was presented to evaluate the performance parameters of the assembly line. The assembly line balancing problem was systematically analysed using industrial engineering techniques of time study, and the corresponding balancing of work elements was performed using the Ranked-Positional Weighs Method (RPWM). The number of workstations required to design an assembly line was kept fixed in accordance with the cycle time requirements. The problem was further extended to multi-objective genetic optimization (MOGA) of the assembly line with objectives of minimizing cycle time and workload variation and maximizing the throughput in terms of line efficiency. The entire cycle time measurement was performed by Predetermined Motion Time Systems (PMTS) as an established work measurement standard. The hypothesis test of cycle time against models was performed to analyse variations in the means and standard deviations of cycle times by Analysis of Variance (ANOVA) using MINITAB © statistical software. In the last part of the paper, discrete event simulation of the process was performed using AnyLogic © software. The simulation provided comprehensive results of standard productivity Key Performance Indicators (KPI), including mean flow times and capacity utilization, to evaluate the pace of the manufacturing system. In future, the correlation between the mathematical model and the discrete event model can be investigated for hybrid-flexible assembly systems .

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Long forging production cycle and low equipment utilization are the important problems currently facing domestic die forging ring production lines. To solve this problem, a simulation optimization method for die forging production line based on Flexsim was proposed. Firstly, the ECRS theory method was used to analyze the bottleneck process of the production line. Then, built a logistics simulation model of die forging production line based on Flexsim, The parallel and cycle processing steps was set reasonable parameters and simulated. Next, through logic analysis, methods such as adding processing equipment, improving equipment efficiency and balancing equipment handling tasks were used to optimize and improve the production line. Finally, compared and analyzed the original and optimized model. The simulation results show that the optimized production line equipment task allocation tends to be rationalized, the equipment utilization rate is increased by 4.34%, and the product cycle time is reduced by 27.65%, and optimized planning and design are achieved.

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In the context of Industry 4.0, a production line must be flexible and adaptable to stochastic or real‐world environments. As a result, the assembly line balancing (ALB) problem involves managing uncertainty or stochasticity. Several methods have been proposed, including stochastic mathematical programming models and simulations. However, programming models can only incorporate a few sources of uncertainty that result in impractical or unfeasible solutions to implement due to overlooked complexities, while simulation is only used to test solutions from deterministic approaches or adjust parameters without maintaining their optimum value. The proposed methodology uses a deterministic mathematical model to minimize the cycle time, followed by the simulation to measure the impact of selected sources of uncertainty on the cycle time. Finally, the optimum value of the stochastic parameters is computed using simulation‐based optimization to maintain the average cycle time close to the deterministic one. A real‐life assembly line balancing problem for a motorcycle manufacturing company is solved to test the proposed methodology. The sources of uncertainty are the tasks' stochastic processing times, inter‐arrival time, the number of workers in each station, and the speed of the material handling system. Results show that the average cycle time is above 2.7% from the deterministic value computed by the programming model when the inter‐arrival time is set to 270 60 s; the processing times are allowed to increase or decrease by 3 s; the material handling system's speed is 1.5 m/s; and the number of workers in cells is between 4 and 6, with a speed of 2 m/s. The reader can download the source code and the simulation model to apply the methodology to other instances.

This study proposes a Centralized IoT-based Process Cycle Time Monitoring System (CTMS) to improve the efficiency and accuracy of line balancing studies in a production line with a conveyor system. The traditional approach of measuring cycle time, typically done by manual observation and recording, can be prone to errors due to human factors such as fatigue or inaccuracies in manual recording. The proposed CTMS utilizes a combination of cutting-edge technologies such as a cloud database, android application, LABVIEW GUI, analogue infrared (IR) sensors, and appropriate controllers to improve the reliability of cycle time measurements. The system continuously monitors the production line and provides real-time data on cycle time, downtime, and other relevant metrics, allowing production engineers to quickly identify bottlenecks and areas for improvement. The results of the study demonstrate the potential of the CTMS to be applied and expanded to actual production lines with conveyor systems, providing a valuable tool for production engineers to develop strategies to reduce cycle time and ensure it is kept in check. The proposed CTMS is expected to have a positive impact on the efficiency and profitability of the production line by reducing downtime and increasing productivity.

With the rapid development of the semiconductor industry, identifying and optimizing bottlenecks is crucial for improving production line efficiency. This paper proposes a method combining Activity Cycle Method (APM) and data visualization techniques. APM identifies key bottlenecks in semiconductor manufacturing by analyzing the continuous uptime of machines and the duration of their activity cycles. Data visualization tools are then used to present these key bottlenecks in an intuitive and actionable manner. Applying both methods to a real-world semiconductor manufacturing environment significantly improves production efficiency and machine utilization, making this method practically applicable in semiconductor manufacturing.

Human–robot collaboration can enhance productivity of production lines and reduce human ergonomic risk. The numbers and types of robots and stations in which robots are allocated need to be determined. Operations should be scheduled carefully when a human and robot work on a part in a station to obtain a feasible operation allocation with the highest efficiency and lowest ergonomic risk. A mixed-integer linear programming model, constraint programming model, and Benders decomposition algorithm were developed to analyse advantages of collaborative robots in assembly lines. An energy expenditure method was used to evaluate ergonomic risk. By scheduling and balancing collaborative human–robot assembly lines, operational advantages and scheduling constraints from human–robot collaboration were studied when immobile and mobile robots are used. Regression lines were developed that can help managers determine how many and what types of robots are best for a line and what the impact of robot mobility on robot and line performance can be. The best configuration for equipping a line with collaborative robots is when (number of robots)/(number of stations) is near .7 and about 37% of robots are mobile. Robots can be efficiently used in lines with both a small and large number of passive resources and in simple and mixed-model lines.

Abstract Workers still perform the bulk of operations in the manufacturing industry. The consideration of the assignment of workers and the reduction of ergonomic risks in U-shaped assembly lines is of paramount importance. However, the objectives of efficient task and worker assignment and a reduction in ergonomic risks are not usually correlated. Moreover, there is limited research in the existing literature into multi-objective approaches in U-shaped assembly lines. We formulate a U-shaped assembly worker assignment and balancing problem to simultaneously minimize cycle times and ergonomic risks. In addition, and due to its simplicity and successful results in flow shop scheduling problems, a Restarted Iterated Pareto Greedy algorithm is designed to optimize both objectives. In this algorithm, a problem-specific heuristic-based initialization is extended to improve the initial solution. Two precedence-based greedy and local search phases are developed to exploit the space around the current solution. Finally, a restart mechanism is proposed to help the algorithm escape from local optima. Comprehensive computational results, supported by detailed statistical analyses, suggest that the proposed multi-objective algorithm outperforms existing methods on a large number of benchmark instances.

Abstract This paper deals with an optimization problem, which arises when a new transfer line has to be designed subject to a limited number of available machines, cycle time constraint, and precedence relations between necessary production tasks. The studied problem consists in assigning a given set of tasks to blocks and then blocks to machines so as to find the most robust line configuration under task processing time uncertainty. The robustness of a given line configuration is measured via its stability radius, i.e., as the maximal amplitude of deviations from the nominal value of the processing time of uncertain tasks that do not violate the solution admissibility. In this work, for considering different hypotheses on uncertainty, the stability radius is based upon the Manhattan and Chebyshev norms. For each norm, the problem is proven to be strongly NP-hard and a mixed-integer linear program (MILP) is proposed for addressing it. To accelerate the seeking of optimal solutions, two variants of a heuristic method as well as several reduction rules are devised for the corresponding MILP. Computational results are reported on a collection of instances derived from classic benchmark data used in the literature for the Transfer Line Balancing Problem.

Multi-sided assembly line balancing problems usually occur in plants producing big-sized products such as buses, trucks, and helicopters. In this type of assembly line, in each workstation, it is possible to install several workplaces, in which a single operator performs his/her own set of tasks at an individual mounting position. In this way, the operators can work simultaneously on the same product without hindering each other. This paper considers for the first time the multi-sided assembly line balancing problem with the objective of minimising the cycle time, proposing a new mathematical formulation to solve small-sized instances of this problem. Besides, a metaheuristic algorithm based on variable neighbourhood search hybridised with simulated annealing is developed to solve large-sized instances. The algorithm is called adaptive because of the adopted neighbourhood selection mechanism. A novel three-string representation is introduced to encode the problem solutions and six different neighbourhood generation structures are presented. The developed approach is compared to other meta-heuristics, considering some well-known in literature test instance and a real world assembly line balancing problem arising in a car body assembly line. The experimental results validate the effectiveness of the proposed algorithm.

In the garment industry, assembly line balancing is one of the most significant tasks. To make a product, a manufacturing technique called assembly line is utilized, where components are assembled and transferred from workstation to workstation until the final assembly is finished. Assembly line should always be as balanced as possible in order to maximize efficiency. Different types of assembly line balancing problems were introduced along with many proposed solutions. In this paper, we focus on an assembly line balancing problem where the upper bound of the number of workers is given, tasks and workers have to be grouped into workstations so that the cycle time is minimized, the total number of workers is minimized and balance efficiency is maximized. With unfixed number of workstations and other various constraints, our problem is claimed to be novel. We propose three different approaches: exhaustive search, simulated annealing and simulated annealing with greedy. Computational results affirmed that our SA algorithm performed extremely good in terms of both accuracy and running time. From these positive outcomes, our algorithms clearly show their applicability potential in practice.

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As Lean Manufacturing and Industry 4.0 aim to create flexible and agile production systems, integrating Industry 4.0 technologies enables manufacturers to optimize their production processes, reduce waste, and improve overall efficiency. This paradigm, known as Lean 4.0, helps companies increase their competitiveness in the market by adapting to changing customer demands and production requirements more quickly and efficiently. One well-known tool is the Yamazumi chart for production cycle times analysis and work balance for bottleneck identification. However, the cycle time data collection approach is a problem. Direct observation of the manufacturing process can be time-consuming and prone to errors. On the other hand, automatic cycle time data collection approaches are more accurate than manual data logging. Still, they can be more expensive to implement since it relies on sensorization and IoT implementation. Also, usually, such sensors may obstruct operator movements. This work presents a Digital Twin framework, DINASORE, for automatically measuring cycle times and tracking the production outputs. Yamazumi charts are generated in real-time to give operators and managers valuable insight into the manufacturing process. The concept is proved through a simple production use case using a laboratory demonstrator. Results show that the solution can help better understand the relationships between workstations for production line performance in real-time while also assessing product quality.

Beyond a simple concern for the company's picture, the global competition requires cost reduction, process rationalization and on-time delivery of customers to remain economically viable and competitive. This needs a continuous improvement and a sustainable strategy, but before this phase, it is necessary to find the influential parameters and put them under control in order to stabilize the process. For this, we referred to Lean Six-Sigma (LSS) tools. In this paper, we concentrate on the wastes that affect the cycle times to produce the good within a process recently implemented. We opted to apply the LSS for the reduction of manufacturing time overruns using the DMAIC concept (Define, Measure, Analyze, Improve, and Control). The proposed case is tested in an aeronautic company. First, we identified the bottleneck in the process. Secondly, we measured the time of each operation of this process and map them via a Value Stream Mapping (VSM). We identified the root causes in the third section. Finally, we established an action plan to improve the process and control the different solutions. As a result, the process cycle efficiency was increased by 16 per cent. Thus, the effectiveness of the proposed method was demonstrated.

The production process improvement is a solution to increase the efficiency of productivity. The improvement is applied to the production process gradually and continuously for the production process balancing. This research uses the cycle time and waste disposal, which are important tools that can be used to analyse the lost process time and waste in all aspects of business. There are guidelines for classifying the types of waste into seven groups. In addition, there are electric torque wrench to one workstation and production line balancing to reduce the cycle time. Finally, the production cycle time is reduced by 14 percents then the production cost is also reduced, and the company can get more profit.

The Sub-Bottleneck concept is introduced in this article for the first time. The literature defines the bottleneck concept through the cycle time in which, as a general rule, the slowest machine with longer cycle time, is classified as a bottleneck. Depending on the cycle time, the machine, the production line, the plant taken into account, etc., the literature has defined the concept of bottleneck in plant, bottleneck in production line, bottleneck in machine, etc. This article presents the Sub-Bottleneck concept for the first time. This concept uses the mini-term, a cycle time of each component that makes up a machine to determine which is the slowest and focus on future improvements that will optimize the efficiency of the production line. In order to validate this proposal, the mini-terms have been implemented in a production line at the Ford factory in Almussafes (Valencia, Spain), made up of 4 welding robots. The tests show the variable nature of the components and that the typical bottleneck studied in the literature does not have to coincide with the Sub-Bottleneck concept.

The garment industry is one of the fastest-paced production environments, where speed and accuracy are crucial for meeting global customer demand. Cycle time plays an important role in satisfying the speed factor as it represents the overall time needed to produce a single piece of a product. Maintaining an appropriate cycle time is key to achieving production line balance. This study aims to analyze the cycle time of men's jacket production on a sewing line in an Indonesian garment industry by comparing the actual cycle time with the standard cycle time based on the operation breakdown for each production process step. Four significant discrepancies in actual cycle time were identified, ranging from 16% to 95%, indicating that the actual cycle time was significantly longer than the standard cycle time. The root cause of the highest cycle time was analyzed using Fishbone and 5 Whys analysis to understand the problem from each factor and sub-factor. Six improvement action plans were proposed and implemented, resulting in a 57.81% reduction in actual cycle time, from 127.66 seconds to 53.85 seconds, making it 17.66% faster than the standard cycle time. Furthermore, the total output per hour increased by approximately 216.67%, from 12 pieces to 38 pieces. This result implies that the study successfully identified the root of the problem on the sewing line and managed to increase production output.

This study explored the implementation of a lean manufacturing approach to enhance productivity in small and medium-sized enterprises, focusing on a cloth manufacturing company as a case study. The objectives of this approach were to improve production efficiency and to minimise waste while ensuring a clean and safe working environment for employees. The methods used include 5S principles to help eliminate waste production in the form of rejects and reworks and assembly line balancing, with a particular emphasis on improving operational efficiency. The largest candidate rule method yielded significant improvements, reducing the number of workstations from 12 to 4, achieving an efficiency of 87.5%, and lowering the smoothing index to 22.05 compared with the existing system’s 29.17% efficiency and 147.78 smoothing index. These findings underscore the potential of lean strategies to optimise production processes and reduce inefficiencies. The study recommends that the company consistently capture, analyse, and monitor key performance indicators to track progress, ensure transparency between management and employees, and sustain growth driven by lean practices. This case study highlights the transformative impact of lean manufacturing on SME productivity and operational excellence.

Waste is an element that must be minimized in order to improve efficiency. This study aims to identify and minimize waste in the automotive mat production process at PT XYZ using a lean manufacturing approach. The main issues identified include high delays between processes, inefficient operator movements, suboptimal material flow. Through field observations, value stream mapping (VSM) analysis, and root cause analysis (RCA) using the 5 Whys method, the primary causes of waste were found to be a non-linear factory layout, accumulation of work in process (WIP), and suboptimal coordination and standardization between workstations. Proposed improvements include the implementation of 5S principles to organize the workspace and reduce buildup, optimization of material flow using floor markings and strategic buffers, as well as production line balancing and the adoption of a pull production system. These improvements are expected to enhance efficiency, reduce waste, and create a leaner and more competitive production process at PT XYZ.

This study aims to improve the productivity of the oil seal manufacturing process at PT XYZ by applying Lean Manufacturing principles through the integrated use of Value Stream Mapping (VSM), line balancing, and the Cost Time Profile (CTP) method. Initial observations showed that the production line experienced significant non-value-added (NVA) activities, particularly waiting time between curing, post-cure, and trimming processes, which resulted in long lead times and high work-in-process (WIP) accumulation. The current state VSM identified curing as the major bottleneck across multiple product families. Improvement strategies were implemented by reducing curing time, adjusting lot size, eliminating deflashing through sensor replacement in trimming machines, reorganizing work sequences, and redistributing operator workloads. The application of line balancing allowed operators to handle multiple machines simultaneously, reducing idle time and improving flow efficiency. As a result, several product families experienced substantial reductions in total lead time, with decreases ranging from 60% to 70%, and increases in value-added ratio. The productivity index also improved, with total output rising from 3,782,400 pcs to 3,808,380 pcs per month, while manpower was reduced from 116 to 100 operators without additional overtime. Analysis using the Cost Time Profile further demonstrated a 3–11% reduction in cumulative cost added per piece. Overall, the integrated Lean Manufacturing and CTP approach successfully enhanced efficiency, reduced waste, lowered production cost, and improved productivity performance in a sustainable manner.

PT. Schneider Electric Manufacturing is a global company leading digital transformation in the fields of energy management and automation. PT. Schneider its prioritizes continuous improvement to control the production process. These include modular and Monoblock sub-assemblies. In controlling effectiveness in a production system it should be a good level on utility (utilization), but after analysed it found low utilization on line production Monoblock many problems with waste from production operators, material transfer and waiting times due to unbalanced processes resulting in less output. (not reaching the target) this has a huge impact on efficiency and productivity. To increase output and utility, use 3 lean manufacturing methods, including Kaizen, Line balancing and Kanban super market to improve overcome problems in the production area. After implementation, utility and output become better.

The manufacturing industry plays a crucial role in the economy development of a country includes Malaysia. Lean manufacturing is a production method that aims to minimize waste and optimize efficiency in the manufacturing process.By applying lean manufacturing,it enablesa company topursues continuous improvement and integration of labor with a clear focus on value adding activities and elimination of waste. However, this concept is still not widely being applied by all type of company or limited in certain aspects only. This study aims to implement lean concept into a medium sized electronic company in Malaysianamed as Company ABC, particularlyto improve the efficiency of the production line. Company ABC is expanding its production line, thus looking forward to implement 8 Waste and VSM to improve the Line Balancing Rate and improve the line productivityfrom 1500 units/ week to 3000 units / week.The clarifications lead to this study is to understand, how the Implementation of Lean Manufacturing can help to improve the production line efficiency, what are the factors that causes producibility issues during design development stage and what are the area of improvement of the manufacturing line of Model X that could be enhanced and applied in this particular project. The goal of this study is to assess the contributionof Lean six sigmain the companyto increase the process line productivity and maximize the efficiency of the production process. Amodelwas developedto simulate the efficiency improvement of the production line after application of Lean Manufacturing.

This study examines how a customized Lean Manufacturing approach can improve production efficiency in the weaving department of a textile company. Weaving operations frequently experience inefficiencies due to machine downtime, waiting time, excessive operator movement, and product defects, which create a gap between targeted and actual output. A quantitative case study design was applied using direct observation, time study, Value Stream Mapping, production records, and maintenance logs. Lean tools including 5S, Total Productive Maintenance, layout improvement, line balancing, poka yoke, and Kaizen were implemented in a pilot weaving area. The results indicate a significant reduction in machine downtime by 53 percent, waiting time by 59 percent, operator movement by 39 percent, and defect rate by 50 percent. Value Stream Mapping analysis further shows that non value added time decreased substantially while value added time remained stable, leading to a 24 percent reduction in total lead time and a 22 percent increase in daily production output. These findings confirm that Lean Manufacturing, when customized to the characteristics of weaving processes, effectively eliminates waste and enhances workflow. The study concludes that integrating Lean with maintenance and process standardization provides a practical strategy to bridge the gap between production targets and actual performance in textile weaving units.    

No abstract available

Distribution Center (DC) operations entail several steps and processes to deliver the product/service to the customer. With processes that entail several steps, it is expected that flow issues will arise. One of the most troubling issues in DCs is the material flow balance, especially in DCs with semi-automated facilities. In such facilities, there is so much reliance on the machines to do the work more efficiently. However, if we do not understand the logic of the machines and the flow, the process become inefficient and we create more bottlenecks and unbalanced production flow between input and output. The objective of this paper is to highlight the DC flow issues in semi-automated flow. More specifically, the paper focuses on the issue of running daily production/material flow based on the number of available people and not the output number required per day. We applied the line balancing approach as one of the lean manufacturing strategies in managing flow. We developed a line balance tool to help practitioners control the production flow more efficiently.

For companies operating within the garment manufacturing industry, having frequent downtimes in their production flows is an extremely common issue. In this context, a balanced production line is required to prevent high waiting times due to limited productive capacity. A well-balanced assembly line allows products to be produced in an optimum time while using less resources, such as machines, materials, or labour, since the right number of products is produced with the exact amount of resources, thus generating savings in production costs. This paper seeks to foster optimum resource allocation through the line balancing tool. Finally, to define a work methodology, best practices were selected, and a procedures manual was developed focusing on Standardization. Both tools were implemented after implementing changes to the company culture by means of the Employee Empowerment tool. As a result of this implementation, workers acquired greater accountability and control over the resources, methods, and equipment of their work areas. After the proposed improvements had been deployed, the company reported an increase of over 20% in production line quality, performance, and efficiency.

This article proposes a production method that aims to increase the manufacturing capacity of a footwear small- and medium-sized enterprise (SME) to reduce backorders. Therefore, an assessment is carried out and delays in production processes, excess product transport time, defective products, and inefficient work methods are identified. This article proposes designing a Lean manufacturing method using the change management approach, whose methodology is composed of six phases. In phase 0, change management is carried out; in phase 1, the company’s current situation is reviewed using the Value Stream Mapping (VSM); in phase 2, the work area is reorganized (implementing SLP and 5S); in phase 3, production is balanced (implementing Line Balancing); in phase 4, continual improvement is established using the Kaizen tool; and finally, in phase 5, the results are evaluated. Through validation, it was possible to confirm that Lean manufacturing tools along with change management increased order deliveries by 82%.

No abstract available

: Companies have to improve their productivity to compete in the ever growing manufacturing company. This study describes the improvement activities of the ELGI ultra company in India using lean tools and line balancing techniques. The objective is to improvise the productivity of mixer grinder assembly by reducing time and worker motion. The time study and line techniques were used in the assembly line. A bottleneck station was recognized to where the operation is inadequate in assembly line layout and workplace organization. The novel workplace layout and better working method for operators were designed and executed. The time after improvement is estimated and the future state of the process has been mapped and the future state map is created. As a result the idle time in each station has been reduced and total work content time in the assembly process has been reduced by eliminating some non-value adding activities.

No abstract available

Minimizing mold changeover time is a critical challenge in the plastic injection molding industry, as it directly impacts productivity, operational efficiency, and competitiveness. This study introduces an integrated approach that combines Lean Manufacturing tools, the DMAIC methodology (Define, Measure, Analyze, Improve,Control), and Single Minute Exchange of Dies (SMED) techniques, enhanced by fuzzy logic and artificial intelligence (AI). The methodology focuses on improving mold changeover processes for the NEGRI BOSSI 650 machine by identifying bottlenecks, transforming internal tasks into external ones, and optimizing workflows to reduce downtime and improve overall efficiency. Key phases of the study included identifying the root causes of inefficiencies through data collection and analysis, streamlining task sequences using real-time process data, and balancing the production line by redistributing workloads and reducing bottlenecks. Fuzzy logic and AI technologies were employed to support decision-making and enhance optimization, ensuring a robust and adaptable framework for continuous improvement. The results obtained were of high impact a 65% reduction in mold changeover time and a 46.8% improvement in Process Cycle Efficiency (PCE) with significantimprovements in terms of the global line balancing. These findings validate the effectiveness of combining Lean principles with advanced technologies such as fuzzy logic in solving Industry challenges, improving resource utilization, and ensuring long-term operational performance. This study just goes to prove that a structured Lean Manufacturing approach combined with innovative tools and automation can drive significant improvement in the plastic injection molding industry, establishing a scalable and competitive strategy for operational excellence.

Global competitiveness and production complexity in modern industries highlight the need for methods to improve efficiency and productivity. Industrial modernization faces challenges such as complex processes, demand variability, and resource limitations, requiring fast and assertive solutions to avoid inefficiencies and loss of competitiveness. In Brazil, the Manaus Industrial Hub (PIM) exemplifies how methodologies and tools such as Lean Manufacturing, Yamazumi chart, Ishikawa, Line Balancing, 5 Whys, 5W2H, and Basic Matrix help to overcome logistics costs and regulatory pressures, optimizing resources and demands. Together with Production Planning, these methodologies and tools minimize costs, maximize productivity, eliminate waste and bottlenecks, and increase competitiveness. Focusing on the manufacture of loudspeakers, this qualitative, exploratory research, through a case study with bibliographic and documentary research, identified practices to optimize processes and solve problems, generating improvements in the production rate with a 21.9% increase in production capacity and labor efficiency with a 10.7% reduction in the number of operators on the line, generating a positive impact on business results. Keywords: Productive Efficiency, Quality, Production Management, Lean.

ABSTRACT Aim to solve all kinds of problem existing in the angle grinder assembly line of the enterprise, such as the unreasonable distribution of work procedures, serious work in progress (WIP) inventory, chaotic production line layout and low assembly line balancing. Value Stream Map (VSM) is used to describe the layout and process diagram of the assembly line and a future value flow chart is established to optimize the angle grinder assembly line in this paper. IE method is utilized to improve the layout of the assembly line and bottleneck location, and the assembly line balance problem in advanced industrial manufacturing is combined with the genetic algorithm (GA). The assembly line improved effectively shortens the product assembly cycle, reduces the inventory and material handling distance, and improves the production line balance.

No abstract available

The comprehensive idea of lean manufacturing emphasizes the identification and removal of wastes related to production. Increased productivity, shorter lead times, improved line balancing, lower work in progress (WIP), higher quality, more adaptable designs, lower costs, etc. are all major objectives of applying lean tools. This study aims to investigate how lean manufacturing techniques might enhance manufacturing efficiency in Bangladeshi RMG sector. The sewing section of a small scale garment industry was the focus of this study. In the study, the existing line layout of a trouser style was examined using value stream mapping which contributed to the identification of numerous waste in the existing layout. In order to change the current status of the sewing section, the workplace was organized by 5S tools to remove wastes identified by VSM followed by altering the conventional longer line layout into three cell of work station as suggested by cellular manufacturing. After implementation of these lean tools, results observed include, production output is increased by 17.80%, efficiency is increased by 12.46%, SMV of product is standardized to 18.68 min., defects per hundred units is reduced to 10% and order change over time is reduced by 37%. The conclusions reached during the execution of this implementation study are realistic and applicable in industries with similar structures. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 27-36

Bangladesh being one of the largest textile producers in the world, the apparel manufacturing industry has to deal with the pressure to produce the maximum possible in the face of low product quality and sustainability standards. This paper examines a case of process improvement in a Bangladeshi apparel industry, which has a middle-scale, by implementing proper use of Value Stream Mapping (VSM), which is a strong Lean Manufacturing tool. A current-state VSM was established through careful time studies and data collection of a T-shirt production line and indicated the many non-value added activities that occurred such as the high waiting time, the unbalanced workflow, and the large amount of work-in-progress (WIP) inventory. On the basis of these observations, a future-state VSM has been put forward with the incorporation of lean intervention like line balancing, standardized work, and better information flow. Future state simulation has shown that there was the possibility that the total lead time would be reduced by 35 percent and overall production efficiency to improve by 25 percent. The current study demonstrates the empirical viability of VSM in the Bangladesh apparel manufacturing industry and gives recommendations that can be implemented by factory managers and policymakers to achieve competitive advantage in the rapidly-changing international market. The paper also fills a research gap in that it portrays an empirical documented case in academia to fill the gap hitherto, in the literature of Lean implementation in the apparel and other manufacturing industries in developing countries.

The textile sector, particularly small and medium-sized enterprises (SMEs), plays a pivotal role in the economy by generating employment and fostering innovation. However, these enterprises face significant challenges, including production inefficiencies and quality control issues. Previous research has explored the application of Lean Manufacturing methodologies to address these challenges, yet there remains a gap in the literature regarding comprehensive models tailored specifically for textile SMEs. This study aimed to fill this gap by proposing a Lean Synergy Production Model integrating Change Management, 5S, Methods-Time Measurement (MTM), and Line Balancing. The textile sector faces urgent challenges such as high defect rates, excessive cycle times, and low productivity. These issues stem from inadequate production methods and poor workplace organization, resulting in increased costs and reduced competitiveness. The proposed model addresses these challenges by streamlining processes and enhancing operational efficiency. The model implementation involved training employees, reorganizing the workspace, optimizing task allocation, and balancing production lines. Key results from the six-month pilot implementation included a 65.67% reduction in delivery delays, a 23.08% decrease in defective products, and a 27.27% reduction in reprocessing rates. Productivity increased by 84.62%, and cycle time was reduced by 25%. These outcomes demonstrate the model's effectiveness in improving production efficiency and quality in textile SMEs. The study's impact extends beyond operational improvements, contributing to academic literature by providing a validated model for Lean implementation in SMEs. Socioeconomically, the enhanced efficiency and quality can lead to increased competitiveness and market share for textile SMEs, fostering sustainable economic growth. Future research should explore the model's adaptability to other sectors and its long-term sustainability. This study calls on researchers and industry practitioners to further investigate and refine Lean methodologies to support the continuous improvement of SMEs globally.

This study applies techniques such as Lean Manufacturing, 5S, Line of balancing, and production bottleneck analysis to tackle the problems of optimizing production resources, minimizing the Work-in-process, eliminating motion waste, and the 5S issues within the manufacturing area. Further, a case study on the production and sales of the shaped plastic packaging company is discussed. The result obtained was that the lead times of all the items improved, the WIP backlog problem was reduced in the production, and the LOB (line of balancing) index of the productions, especially the LOB of the “TET TIN” and the “SPRING ROLL,” increases of 19.1% and 12.09%, respectively.

In response to global manufacturing competition for manufacturing excellence, this study applies lean production principles to optimize the assembly line of the glass product in Company Z, a glass manufacturer. Through time study, process flow analysis, both-hands operation optimization, and Flexsim simulation, the research achieves an 86.19% line balancing rate, reduces cycle time to 63.76 minutes, and implements 6S management for enhanced on-site organization. The findings validate the effectiveness of lean methodologies in resolving production imbalances.

The objectives of this research are to study the current process and improve the production process of pantograph jack by using Lean manufacturing techniques. The Lean tool used is Value stream mapping (VSM). The study starts with collecting data from the upstream to downstream to create a Current-state VSM to identify wastes and problems. The first problem was the low efficiency of the production process due to the high lead time of 7 days 8 hours. The ratio of value-added time to the lead time was only 0.026%. The next problem was overproduction because the cycle time is 69.54 seconds/piece while the takt time is 140.15 seconds/piece, which is 46.62% of the takt time, resulting in the early stoppage of the production line to prevent over inventory of finished products. After identifying the problems, the next step is to define production process improvement approaches by creating a Future-state VSM. The tools for the improvement included the Kanban system, Supermarket, Line balancing, and Cellular manufacturing then a simulation model of both current and future states was created to compare the results before and after the process improvement. The result from the simulation shows that the total lead time was reduced to 4 hours 39 minutes or a decrease of 97.54% from the current state. The ratio of value-added time to the lead time was increased to 1.304%, which is 49.39 times more than before the improvements, and the cycle time was increased to 133.02 seconds/piece, which is 94.91% of the takt time.

Over the last few years, the Peruvian footwear industry has been affected by the increase in imports, mainly from China, with an FOB value that has risen to 516 million dollars, generating a deficit in the trade balance and forcing producers to become more competitive. Therefore, the national industry seeks to raise the standards of productivity and quality of its products, increasing the service level offered. In this research, it is proposed an improvement model that allows optimizing order fulfillment, which integrates lean manufacturing tools, including 5S, line balancing, and standardized work. After the implementation of the model, the cycle time will be reduced by 27.27%, the number of defective products in the assembly area will decrease by 8. 90%, and in the flooring area by 19.91%. Due to this, it is possible to increase the value of OTIF by 44.48%.

This article presents a study on the estimation of the Standard Minute Value (SMV) of the blazer production process through work study for a well-balanced assembly line. The objective of the study was to optimize the production process by identifying inefficiencies, reducing the SMV, and optimizing the required manpower. Data were collected through time and motion studies and process analysis in a garment factory. The initial SMV for blazer production was determined to be 76.30 minutes. Through the implementation of work study and line balancing techniques, the SMV was successfully reduced to 73.15 minutes. Additionally, the manpower required for the assembly line was reduced from 139 workers to 136, leading to improved productivity and cost savings. The results show how work study and line balancing may be used to optimize the blazer manufacturing process. The study underlines the need of putting lean manufacturing ideas into practice to reduce waste and boost productivity. Employee engagement and motivation were increased by involving them in the line balancing process, which was essential for obtaining good results. This research contributes on work study and line balancing in the garment industry. The findings provide practical insights for garment manufacturers seeking to optimize their production processes. By implementing the recommended approaches, companies can reduce SMV, optimize manpower requirements, and improve competitiveness. This research enhances our comprehension of work study and line balancing within the apparel industry. The results provide garment manufacturers with valuable insights to streamline their production processes. Through the adoption of recommended strategies, companies can lower Standard Minute Values (SMV), fine-tune workforce requirements, and improve their competitive edge

This applied research article explores the application of Mixed-Integer Linear Programming (MILP) to address line-balancing challenges in the garment industry, focusing on optimizing production processes under multiple constraints. By integrating MILP with Lean Methodology principles, the study demonstrates significant improvements in operational efficiency and cost-effectiveness. The case study, conducted in collaboration with Prof Dr Ray WM Kong, highlights the successful implementation of MILP using IBM CPLEX Studio to optimize production order quantities across online and offline operations. The results reveal a remarkable reduction in labour costs, exceeding 50%, while effectively managing resource capacity and demand constraints. This study not only validates the theoretical underpinnings of MILP in resolving line-balancing issues but also underscores its practical applicability in modernizing garment production. The findings contribute valuable insights into the potential of advanced optimization techniques to enhance competitiveness and sustainability in the garment industry. This abstract succinctly captures the essence of the research, emphasizing the methodology, results, and significance of the study.

The assembly line balancing problem is the process of assigning tasks to workstations in such a way as to reduce lost time and increase the efficiency of the line, while respecting certain technology-driven precedence relationships and constraints such as capacity. Due to the large and complex design of assembly lines, the smallest disruption and imbalance at any one station can affect the entire line performance. To minimize or avoid such disruptions, the assembly line must be balanced. This study focuses on a detailed analysis process to identify specific problems and disruptions in the assembly line. The line balancing application in the study was carried out as a case study in a furniture company's bed base production line. The production line was first surveyed and workflows, task durations and precedence diagrams were identified. The assembly line balancing problem was solved by using Positional Weighting, Largest Candidate and Kilbridge-Wester methods and the results were compared. Thanks to the balancing studies, the workflow has been streamlined, the use of workstations has been optimized and the production processes have been managed more effectively.

Assembly line efficiency is one of the most important parameters that determine the overall efficiency of a manufacturing company. The production of a product under optimum conditions is ensured by a balanced assembly. With a balanced assembly line, machinery, material and labour costs are reduced. Within the scope of this research, real data about the daily production capacity and assembly line efficiency of a company producing Emergency Luminaire were taken, the same assembly line was balanced with 4 different Heuristic ALB methods and the results were compared. According to the results obtained, a high line efficiency of 93.955% was achieved using the Hoffman, Comsoal and Moodie&Young (M&Y) methods, and 84.414% was achieved with the Ranked Positional Weight (RPW) method. As a result of this, it was observed that the daily production capacity increased from 250 units to 375 units. As a result of the study, it was revealed that the efficiency of the existing assembly line and accordingly the daily production capacity increased. In addition, the study results of this assembly line were taught to an artificial neural network model for training purposes, and the work station results of the operations of a different assembly line were obtained with 99.940 accuracy. In this context, it has been revealed that the artificial neural networks method can be used in addition to the use of the heuristic method in the solution of ALB problems.

No abstract available

The Indonesian automotive industry has become an important pillar in the country's manufacturing sector. As production capacity increases, problems will also increase, including disparities in the level of efficiency and productivity of each sub-sector of the manufacturing industry in Indonesia. This occurs due to not having a good process path, such as the uneven distribution of work tasks / machines in the work process so that it is possible to harm the company, so that a solution is needed to increase the efficiency of the production line. This study aims to improve the efficiency of the SL type cabin assembly line production by implementing the Ranked Positional Weight (RPW) method. The research stages include data collection, data analysis, data processing and evaluation. Based on the simulation results, SL type cabin using the Ranked Positional Weight (RPW) method has increased track efficiency by 4.69% from the initial condition, the track efficiency of 75.02% increased to 79.71%. The increased efficiency of the production line can also reduce idle time in the SL type cabin assembly. Based on the calculation results, it can be concluded that by implementing the Ranked Positional Weight (RPW) method can reduce 1 production operator for the SL type cabin assembly so that it can increase the efficiency of the SL type cabin assembly line by reducing idle time.

This study aims to balance the garment line of the polo shirt operation by utilizing line balancing techniques. These techniques are employed to enhance the efficiency and productivity of the Abay garment industry. Observational methods and stopwatch timing were employed to collect data on the processing times for each operation. Subsequently, the standard allowed minute was determined based on work measurement principles. To assess statistical significance and identify appropriate expressions for the existing simulation modeling, all collected data underwent statistical analysis using the Arena input analyzer. Moreover, Arena simulation software was employed in this research to simulate and evaluate the effectiveness of both the current and proposed polo shirt sewing line models. Building upon the existing model, alternative improved models were proposed. The alternative models yielded significant improvements in multiple performance metrics, including output, capacity utilization, waiting time in queues, and number of products waiting. These improvements indicate an increase in productivity resulting from the implementation of balanced production lines. The outcomes of the improved scenario included an increase in output from 288 to 381, a rise in line efficiency from 39.06 % to 55.64 %, and an enhancement in labor productivity from 54.25 % to 66 %. Additionally, the cost of labor was reduced by 15.63 %, while revenue experienced a notable increase of 30 %.

No abstract available

The mixed-model assembly line balancing problem (MMALBP) in multi-demand scenarios is investigated, which addresses demand fluctuations for each product in each scenario. The objective is to minimize the sum of costs associated with tasks allocation, workstation activation, and penalty costs for unbalanced workloads. A mixed integer programming model is developed to consider the constraint of workstation space capacity. A phased heuristic algorithm is designed to solve the problem. The computational results show that considering demand fluctuations in multiple demand scenarios leads to more balanced workstation loads and improved assembly line production efficiency. Finally, sensitivity analysis of important parameters is conducted to summarize the impact of parameter changes on the results and provide practical management insights.

No abstract available

Companies strive to be more efficient and constantly increase manufacturing productivity to stay competitive. The Overall Equipment Effectiveness (OEE) is a relevant performance measurement that companies use to monitor efficiency, quality, costs, and the capacity of their production lines. A case study in a pharmaceutical company was conducted to see if additional methods alongside the OEE could help improve the production planning, capacity utilization, and output of a packaging manufacturing line regarding production speed, demand size, and cost per item. Therefore, the study utilized theoretical concepts from the literature with empirical data to develop a simulation model for this specific manufacturing system. A time study and a discrete event simulation were used, and the solution showed acceptable and coherent to real numbers. In addition to bottleneck identification, the simulation enabled the estimation of an optimal number of operators and the gains achieved by implementing changes in the manufacturing processes. It was concluded that the simulation model could help to improve the production planning and, subsequently, the capacity utilization and output of the manufacturing line.

Line balancing is always a big problem appearing in industrial production. Manual balancing of industrial sewing products takes a long time to give results, which depends on the experience of the sewing line manager, moreover, the efficiency is not necessarily optimal. Digital conversion will help find a solution to balance the sewing line more quickly, accurately, and optimally. This study presents the statements of the problem of balancing knitted garment lines in the industry with the line balancing process according to the method of Hanoi University of Science and Technology (HUST) and BSL-HUST-1 software, which is the software designed and built by our research group. For the balancing calculation, three groups of input data were defined for the comparison of balancing efficiency among the HUST method, the software method, and the method used traditionally by the companies. The line's capacity is determined as the total production amount in a shift, and the shift time is figured accordingly following each factory's rules. The total number of workers is an essential factor. Also, the cycle time is one of the important factors for balancing the sewing line.

: In order to solve the motor production line balance is low, low production efficiency, production capacity can not reach the expectations, the high rate of worker operation error. In this paper, the industrial engineering process analysis method, the ECRS principle, the "5W1H" and the implementation of 6S field management are adopted to improve the overall production operation of the production line and the operation content of some work units. The balance rate of the production line was increased from 71.72% to 78.23%. In terms of balance rate, the improved production line has a better effect than before, which makes the production line reach the scientific management level and provides a reference for the improvement of similar production line balance. It has important theoretical significance for the research results in manufacturing field, and has practical value for enterprises.

The Indonesian automotive industry has become an essential pillar in the country's manufacturing sector. As production capacity increases, problems will also increase, including disparities in the level of efficiency and productivity of each sub-sector of the manufacturing industry in Indonesia. This problem occurs due to the need for a good process path, such as the uneven distribution of work tasks machines in the work process so that it is possible to harm the company, so a solution is needed to increase the efficiency of the production line. This research aims to improve production efficiency, particularly concerning the use of electricity costs and operator wages on the cabin type S L assembly line, by applying the Ranked Positional Weight (RPW) method. The research phases include data collection, analysis, processing, and evaluation. Based on the SL-type cabin calculations using the RPW method, the track efficiency improved by 4.69% from the initial conditions, while the track effectiveness increased by 75.02% to 79.71%. Increased the production line efficiency has impacted on the decrease in production costs Rp. 13,827,249/month.

No abstract available

This study investigated whether data-driven industrial engineering models improved efficiency and reduced risk in U.S. apparel supply chains, a domain with high clockspeed and volatile demand where causal mechanisms were often under-specified. The purpose was to estimate associations between the adoption intensity of analytics-supported line balancing, statistical process control, stochastic inventory and scheduling, and simulation-backed capacity planning and two outcome families: operational efficiency and operational or supply risk. The design was quantitative, cross-sectional, and multiple-case. A focused narrative review of 44 peer-reviewed studies informed construct definitions and item wording. The sample comprised 208 analyzable cloud and enterprise cases drawn from brand owners, contract manufacturers, and distribution partners, with managers as respondents and optional KPI uploads. Key variables included adoption, process standardization as a mediator, supply chain complexity as a moderator, and composites for efficiency (on-time in-full, order cycle time, throughput, unit cost, rework, appropriately reverse coded) and risk (disruption frequency, lead-time variability, stockout, returns or defects, compliance). The analysis plan specified descriptives, correlations, ordinary least squares with robust or clustered standard errors and a common control set (size, automation, capital intensity, market segment, nearshoring ratio, supplier reliability, demand clockspeed), moderated models with mean-centered interactions, bootstrapped indirect effects for mediation, and robustness checks including alternative index constructions and influence diagnostics. Headline findings indicated that higher adoption was associated with higher efficiency and lower risk after controls, with partial mediation through process standardization and attenuation of effects at very high complexity; results were stable across robustness specifications. Implications for practice included sequencing capability building from measurement to modeling to routinized execution, modularizing product and network complexity, and treating analytics pipelines as governed infrastructure while interpreting estimates as associations rather than causal effects.

No abstract available

The following work aims to show how a combination of continuous improvement (CI) and Lean tools can reduce waste and process variability along an air-conditioned pipe production line (PL), calculate its capacity, and improve its efficiency to achieve the expected productivity. A variability study focused on the PL’s balancing was conducted to identify and reduce possible bottlenecks, as well as to evaluate the line’s real capacity. Several layout improvements were made to upgrade the line’s operational conditions and reduce unnecessary movements from the workers. The Constant Work-In-Progress (CONWIP) methodology was also applied to ease the component’s production management in the preparation stage. Additional modifications were implemented to support production and to contribute to the increases in efficiency, quality, and safety on the line. The results revealed an increase in the line’s capacity, associated with an efficiency rise from 28.81% to 47.21% from February to June 2023. The overall equipment effectiveness (OEE) in the same period increased by 18%. This demonstrates that, by interactively applying a mix of tools and methodologies, it is possible to achieve better performance of production lines. This knowledge can help scholars and practitioners to apply the same set of tools to solve usual problems in cell and production lines with performance below expectations.

In most manufacturing companies, the layout designs and line balancing problems are often based on personal experience and made without following a theoretical methodology. By applying those ad-hoc solutions, various problems may arise when quick changes of capacity or any other constraints occur. This work was developed for a Portuguese SME in the electronics industry, that had some changes at the production level, which caused limitations in terms of space on the factory floor. Furthermore, it was also revealed that an existing production line with high production rates was gradually losing efficiency. Bringing these two issues together, the idea was to design a new plant layout to improve the performance of this production line, considering the new space constraints. To increase the production line efficiency, decisions such as the number of workers and assembly task assignment to stations need to be optimized to increase its throughput and decrease cost. An integer linear programming model was developed and used to solve the balancing problem. Considering six different optimization criteria, five variants of the model were tested. Using the best solution according to predefined Key Indicators Performance, the layout was developed using the Systematic Layout Planning approach.

No abstract available

Manufacturer of cattle and goat feed, faces challenges in meeting increasing market demand. To increase production capacity, the company can implement several industrial engineering-based strategies. First, time and motion analysis can be conducted to identify and reduce non-productive activities, thereby improving work efficiency. Second, production line balancing is important to ensure equitable workload distribution, reduce waiting time between processes, and maximize output. Third, the implementation of lean manufacturing can help eliminate waste in the production process, such as overproduction and excessive waiting time. In addition, the use of automation technology in the feed processing process can accelerate production and reduce human error. The implementation of these proposed improvements is expected to significantly increase the production capacity.

No abstract available