seru生产

As manufacturing faces evolving customer demands, the integration of Industrial Internet of Things (IIoT) networks is crucial for enhancing production flexibility. In this context, the Seru Production System (SPS) has emerged as a highly adaptable production mode and emphasizes the strategic assignment of cross‐trained workers, particularly in hybrid configurations combining divisional and rotating serus. This paper proposes a novel bi‐objective mathematical model incorporating learning effects to minimize makespan and balance workloads among workers. With the development of Artificial Intelligence Generated Content (AIGC) empowered big models, new breakthroughs have emerged in industrial manufacturing decision‐making. These models utilize deep learning for foundational content processing and leverage reinforcement learning to optimize strategies. This process provides robust support for achieving efficient decision optimization. Building on the concepts of AIGC big models training, this study employs reinforcement learning to refine the results of multi‐objective genetic algorithms, thereby improving the solution capability of the bi‐objective model. Experimental results demonstrate that the proposed algorithm effectively provides optimal strategies for tuning crossover and mutation operations. Additionally, numerical experiments offer insights into the formation of hybrid SPS configurations.

Most studies on seru production scheduling focus on static systems and ignore worker fatigue, limiting their use in flexible manufacturing. To address this, we investigate a multi-batch reconfigurable seru production system (SPMRSPS) that incorporates fatigue effects. We formulate a mixed-integer programming model to minimize the makespan, the idle time of multi-skilled workers, and storage costs. To solve the model, we propose a multi-objective adaptive evolutionary algorithm with a heuristic strategy (MOAEH) that utilizes a distance similarity-based seru formation strategy and adaptive genetic operators. Numerical experiments show that MOAEH outperforms several state-of-the-art algorithms, providing an effective tool for SPMRSPS scheduling.

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The flexibility and responsiveness of seru production have caught the attention of manufacturing and electronics industries. However, multi-skilled worker assignment poses a crucial and challenging decision-making problem for seru production systems. The existing literature on this problem for pure seru production systems primarily focuses on improving efficiency indexes, which often leads to an unbalanced workload among workers. To address this issue, this article investigates multi-skilled worker assignment for a hybrid assembly line-seru production system that comprises divisional serus and a short assembly line. To balance workload and optimize production efficiency, a bi-objective integer nonlinear programming model is developed. This model jointly optimizes worker-to-seru, worker-to-line, batch-to-seru, task-to-worker, and the processing sequence of each batch. A meta-heuristic method,  combining Non-Dominated Sorting Genetic Algorithm II (NSGA-II) with Multi-Objective Simulated Annealing (MOSA), NSGA-II-MOSA, is designed to solve the model. The results of numerical experiments demonstrate that the proposed model and solving method can greatly reduce workload imbalance while maintaining production efficiency. Moreover, NSGA-II-MOSA provides better Pareto solutions than three well-known multi-objective optimization approaches.

The Seru production system is an innovative assembly system that combines the flexibility of shop floor production and the high efficiency of assembly lines. In addition to its advantages, seru-type production has the disadvantage that all the tasks required for the assembly of a product are completed in a yatai by a cross-trained worker. This results in a higher risk of production errors compared to assembly lines. In order to mitigate these risks, a real-time control system is considered necessary. Recognition of assembly tasks is needed to prevent quality defects in the Seru production system. To meet this need, in this study, a skeleton-based deep learning hybrid Convolutional Neural Network-Bi-directional Gated Recurrent Unit-Convolutional Neural Network (CNN-BiGRU-CNN) assembly task recognition model is developed. In this study, a two-stage data augmentation approach is proposed to improve the performance of the model developed by utilizing the worker skeleton data obtained with Mediapipe Holistic infrastructure from the assembly video collected from the seru production system. The effectiveness of the model was evaluated by comparing it with Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Bi-directional Long Short Term Memory (BiLSTM), BiGRU, and hybrid models created by combinations of these models. The proposed data augmentation approach improved the performance of all the models compared in the study. With the proposed hybrid CNN-BiGRU-CNN model, the best performance was achieved with a prediction accuracy of 96%.

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PurposeThe paper formulates a bi-objective mixed-integer nonlinear programming model, aimed at minimizing the total labor hours and the workload unfairness for the multi-skilled worker assignment problem in Seru production system (SPS).Design/methodology/approachThree approaches, namely epsilon-constraint method, non-dominated sorting genetic algorithm 2 (NSGA-II) and improved strength Pareto evolutionary algorithm (SPEA2), are designed for solving the problem.FindingsNumerous experiments are performed to assess the applicability of the proposed model and evaluate the performance of algorithms. The merged Pareto-fronts obtained from both NSGA-II and SPEA2 were proposed as final solutions to provide useful information for decision-makers.Practical implicationsSPS has the flexibility to respond to the changing demand for small amount production of multiple varieties products. Assigning cross-trained workers to obtain flexibility has emerged as a major concern for the implementation of SPS. Most enterprises focus solely on measures of production efficiency, such as minimizing the total throughput time. Solutions based on optimizing efficiency measures alone can be unacceptable by workers who have high proficiency levels when they are achieved at the expense of the workers taking more workload. Therefore, study the tradeoff between production efficiency and fairness in the multi-skilled worker assignment problem is very important for SPS.Originality/valueThe study investigates a new mixed-integer programming model to optimize worker-to-seru assignment, batch-to-seru assignment and task-to-worker assignment in SPS. In order to solve the proposed problem, three problem-specific solution approaches are proposed.

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ABSTRACT Seru production system is an innovative assembly system and combines the flexibility of job shop production and high efficiency of assembly lines. This production system easily adapts to product replacements, reduces product wastages by eliminating work-in-process inventories and delay time, and provides enterprises with a competitive advantage through reducing the operating costs, required workforce, and space. Besides these advantages, the disadvantage of seru type production is that, in assembly lines, specific tasks are completed in specified stations, whereas in seru production, the tasks required for the assembly of a product are completed in a yatai by a cross-trained worker. This, in turn, results in a higher risk of production errors. Accordingly in this research, a conceptual model is proposed to monitor and control multiple factors of the production process like worker, environment, assembly tools, ergonomics, storage and inventory and issue warning for prevention of process and quality errors in seru production by use of advanced analytics based on deep learning. In addition to providing support to the worker, the proposed Smart Seru Production System Model will assist system participants in obtaining and understanding data about the production processes and reacting quickly based on this information.

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We present the flexibility of a human-centered production system called the seru production system (SPS). A theoretical framework for analyzing the flexibility of an SPS is proposed based on the input-process-output (IPO) system view. The enabling effect of workforce configuration on the flexibility of an SPS is explained. The flexibility of SPS is identified to be the capability of an SPS to have inclusiveness and variability. The inclusiveness shows the capability of an SPS to control-variability-with-stability, and variability presents its ability to control-variability-with-variability, which correspond to structural flexibility (SF) and reorganization flexibility (RF), respectively. We reveal that the SPS adopts SF as the main strategy to satisfy most demands and uses RF as an auxiliary means to capture unforeseen demands. In addition, our work reports the strategies for implementing SPS flexibility including structural flexibility strategy, reorganization flexibility strategy, and hybrid flexibility strategy.

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Abstract In this article, four scheduling problems with controllable processing times and resource allocation in seru production system (SPS) are first studied. The objective is to minimize the total processing cost plus scheduling measures, which are the total waiting time, the total absolute differences in completion times, the total absolute differences in waiting times and the total earliness and tardiness, respectively. A general exact solution method is proposed to show that these four problems can be transformed into assignment problems and solved in polynomial time if the number of serus and processing times are given in advance. Computational experiments are made finally, and results indicate that the exact solution method is effective to return optimal schedules for four seru scheduling problems. Further, the schedule flexibility of SPS can be enhanced by considering controllable processing times, and workload balance is also need to be concern in seru production practice.

Abstract This study explores the problem of workforce scheduling in the seru production environment where operations are performed within independent serus, so-called assembly cells. Although the seru system attracts more attention in recent years, the addressed problem remains scarcely investigated in the existing literature. To analyze the problem in detail, first, a comprehensive optimization model is proposed by placing an emphasis on achieving Shojinka, which is a Japanese word. Subsequently, the structural properties, the lower and upper bounds are presented to aid the understanding of the problem. The model is solved optimally for small-sized problems; however, several algorithms with two different initial population generation procedures are developed for large-sized problems due to the complexity of the problem. The impact of achieving Shojinka along with the workers’ heterogeneity is investigated in detail through experimental design. To this end, four different scenarios are constructed and a distinct algorithm is devoted to each scenario for the comparison purpose. According to the results, allowing interseru worker transfer leads to a considerable decrease in the makespan. This study contributes to the existing academic literature by presenting several insights regarding the implementation of operational strategies on the seru production system.

In recent years, the interest in seru production system (SPS) has increased to enhance the flexibility of production systems. Because the worker resource in an SPS is critical for adapting to changes in demand, this study focuses on workforce-related operational strategies rarely considered for SPS. To this end, for the first time in the literature, a bi-objective workforce scheduling problem is addressed by considering the interseru worker transfer in SPS. A novel optimisation model is proposed to achieve two objectives, that of minimising makespan and reducing workload imbalance among workers. Because it is proved that the problem falls within a non-deterministic polynomial-time hardness (NP-hard) class, non-dominated sorting genetic algorithm-II (NSGA-II) is employed to solve large-sized problems. For small-sized problems, the second version of the augmented ε-constrained (AUGMECON2) method is implemented and Pareto-optimal solutions are obtained. A set of evaluation metrics is considered to compare two different operational strategies in terms of the desired objectives. The computational results indicate that allowing worker transfer leads to better results for all metrics. The main contribution of the present study is to provide a novel optimisation model for the addressed problem to compare two operational strategies by considering the heterogeneity inherent of workers.

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Sponsored by expedient technologic innovation, consumers frequently expect manufacturer offerings to exhibit extensive product variety and regular product advancement. These expectations have rendered many traditional production practices less applicable. Chiefly impacted is the notion of mass produced, low-variety artifacts via massive assembly lines. These operations have difficulty meeting the high-customization, short life-cycle requirements imposed by contemporary demand. Many industries and organizations have begun the transformation from these rigid assembly mechanisms to a more versatile, cellular production strategy known as seru production. To facilitate this transition, methods are needed to aid manufacturers in appropriately selecting and arranging seru system components, a critical step in seru system design. Herein, a generalized model is proposed utilizing a system-of-systems architecting approach to determine the component assembly best suiting the needs of the manufacturing entity. Candidate architectures are generated and evaluated using a multi-objective genetic algorithm from which a preferred alternative is selected through a fuzzy inference system. Directing this genetic algorithm, domain-independent objectives are presented, maintaining applications to most seru production design scenarios. The proposed method is then applied to a camera production example, culminating in the identification of a well-performing architecture. The presented method should find increased use as organizations further adopt this flexible production methodology.

For the manufacturing companies that take make-to-order production, due to limited production capacity, accept and process all orders may result in delayed delivery of some orders, so the companies not only have to pay a large amount of delay penalty, but also may lose a large number of customers. In this case, the order acceptance and scheduling problem in the seru production system with limited processing capability and the promised delivery time are considered in this paper. At the same time, lot-spitting is also concerned, and an integrated decision-making model for order acceptance and scheduling with the goal of maximizing net profit is established. An improved genetic algorithm is designed for the proposed model. In the end, a numerical example is applied to illustrate the validity and feasibility of the proposed model and algorithm.

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The system reliability plays an important role for evaluating the stability of a production system. Seru production system has been proven to be more flexible, efficient, and responsive than traditional assembly lines. This paper measures the system reliability of a seru production system, in which the capacity of each worker is not certain but stochastic by reason of the possibilities such as worker absences, contingent physical and/or emotional influences. In today’s fast-paced society, time has naturally become an important factor in business competition. In this paper, the reliability is defined as the probability that a seru production system with stochastic capacity can satisfy the makespan for the demand within the due date. An efficient solution method is designed to acquire the system reliability for seru production systems. A numerical example with three cases (i.e. three different seru construction) is presented. The relevant higher reliability will be found by comparing the results under different seru constructions and different order allocations in case one.

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Seru production system (SPS) is now widely adopted in Japanese manufacturing firms to deal with the dynamic uncertain demand. Multi-skilled workers and compact layout make SPS takes the advantage of both efficiency and flexibility. In this paper, we compared the fulfill rate of three skill levels of SPS (single-skilled, long chain two-skilled and full-skilled). The purpose of this paper is to testify which skill level performs best for implementing SPS under fluctuated demand. On this basis, we consider the efficiency decline factor (EDF) which represents the processing time of a worker will be longer when he serves more product types. Scenario decomposition method is adopted to estimate the fulfill rate of three skill-leveled SPS. Numerical experiments show that fulfill rate of long chain two-skilled SPS is almost the same as full-skilled SPS, while single-skilled SPS' fulfill rate decline significantly with the growing of demand fluctuation. Moreover, with considering of EDF, long chain two-skilled SPS performs the best among them.

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Seru production systems have demonstrated excellent rapid response capabilities in both stable and uncertain environments. This study reveals that when compared to the Toyota Production System, the seru production method improves rapid response capabilities by 20% and 50% in stable and uncertain environments, respectively. The underlying reasons for this improvement were unclear, so this became a focus of this paper. Static and dynamic Just-In-Time Organization Systems are used to investigate both the flexibility and efficiency of seru production systems under stable and uncertain conditions. Findings show that the rapid response capability of a seru system is driven by the substitution effect of parallel serus. Efficiency is relatively easy to achieve in stable environments but is more challenging in unstable conditions. Therefore, this study explored methods to achieve high efficiency in seru systems under uncertain environments. A stochastic gradient algorithm and a dynamic allocation algorithm are proposed. Experimental results demonstrate that the proposed methods outperform traditional newsvendor models and can achieve near-optimal performance.

The seru production system introduces a novel re-configurable production paradigm. Although random variations in worker processing times (WPTs) are inevitable due to fluctuations in worker efficiency and machine performance, few studies address uncertain factors in seru production problems (SPPs). Another challenge lies in the characterisation of nonlinear seru processing times (SPTs), leading existing exact algorithms to enumerate an exponential number of possible seru formations. To mitigate the uncertainty in WPTs, this paper proposes a decision-dependent robust optimisation model, which characterises both endogenous and exogenous uncertainties. The budget that regulates the conservatism of the uncertainty set is modelled as an affine function to decision variables. To tackle the robust SPP (RSPP) with infinite nonlinear constraints, we propose an exact equivalent reformulation and a dual-level robust Q-learning-based cooperative coevolution algorithm (DRQL-CCA). First, by analysing its structural properties, we propose the first equivalent mixed 0-1 second-order cone programming reformulation, which achieves up to one order of magnitude speedup compared with existing algorithms. Second, for large-scale problems, we design a robust Q-learning model to guide the search of the DRQL-CCA by incorporating the worst-case system performance as state and reward indicators. Moreover, the dual-level Q-learning approach provides a divide-and-conquer architecture by decomposing the decision-making process into sub-problem selection and optimisation stages. Compared with the deterministic model, the proposed robust model can significantly reduce the variance of the schedule. Numerical comparisons with state-of-the-art algorithms also verify the superiority of the proposed DRQL-CCA.

The hybrid seru manufacturing mode widely exists in many real-world production enterprises, where workers are usually partially cross-trained due to high-training costs and employee turnover. However, the hybrid seru system scheduling problem considering worker heterogeneity (HSSWH) has rarely been studied in academia. To fill the gap, this article introduces a branch-and-bound enhanced cooperative evolutionary algorithm (BBCEA) to solve the HSSWH. Three core search components and an evaluation component are proposed in BBCEA, which are crafted to be problem-specific. In the exploration search component, a probability model sampling method and crossover collaborate to generate offspring with high quality and diversity. In the exploitation search component, five knowledge-based operators collaborate with a knowledge-guided operator selection strategy, which is designed by fully utilizing the problem properties and feedback information. In the exact search component, a branch-and-bound method is designed to solve the bottom layer subproblem precisely, which can greatly improve the effectiveness of the algorithm. In the evaluation component, a look-up table method is proposed to reduce computation effort by avoiding duplicate calculations. Numerical experimental results validate the superiority of the BBCEA in addressing the HSSWH, which can obtain the best solution on 95% of the instances compared with the state-of-the-art algorithms.

Seru production is a human-centered, flexible manufacturing system well-suited to rapid market changes. In this paper, we investigate how to design a high-performance divisional seru within a Seru system considering non-preemptive and discrete stations. We begin by identifying the key factors of a seru and develop an Information Diffusion Game (IDG) model based on undirected graphs. We establish the existence of a pure Nash equilibrium (PNE) and demonstrate its implications for workload balance among workers. To evaluate performance, we conduct simulation experiments and compare the throughput of seru configured under the PNE design with that of cellular bucket brigades. The results show that the PNE-based design achieves an average throughput improvement of 21.13 percent relative to alternative strategies, with particularly strong performance when station processing times are highly variable. This research addresses a critical gap in the micro-level design of seru systems and provides a theoretical foundation for investigating the design of other seru configurations.

High flexibility is an important feature of seru system that has received less attention. In this paper, we discuss how to do such flexible seru system formation, especially focusing on the strategic decision phase. We formulate the flexible seru system formation problem (FSFP) as a nonlinear programming model to evaluate flexibility performance in terms of flexibility–investment cost and flexibility–loss cost. To exactly obtain the optimal solution of the FSFP, we transform the nonlinear model into a linear one and solve it with Gurobi solver. For the large-scale problem, we proposed a parallel Master–Slave adaptive genetic algorithm (PMSA-GA) by transforming it into a two-stage stochastic programming model. The adaptive selection is used to improve the quality of solutions in PMSA-GA. To reduce the computational time, multiple populations of seru formation evolve in parallel with the assistance of the Master–Slave mechanism. Extensive experiments are tested to evaluate the performance of the proposed model and algorithm, and the effect of cost parameters on the system performance is discussed. The results show that the FSFP model takes the property of dynamic demand into account and is more suitable for dynamic demand environments than the task-oriented seru formation (TOSF) strategy from the previous literature.

ABSTRACT Lot splitting is one of the effective technologies of time-based strategy and has been widely studied in a variety of production environments. Nevertheless, the literature on its application in seru production has been highly scarce until now. Seru system is composed of simple equipment and multi-skilled workers, which can be quickly converted from the traditional assembly line to seru units. As an innovative production mode, seru production inevitably allows applying lot splitting in the real world. Therefore, a multi-objective model is studied for line–cell conversion with lot splitting, aiming at determining the trade-off among makespan, inter-seru system balancing, and intra-seru system balancing. Due to the proposed model's NP-hard nature, an improved NSGA-II was developed to solve it. Finally, extensive numerical simulations are conducted. Compared to no lot splitting, lot splitting is improved by 4.2% and 3.7% in terms of inter-seru system balancing and makespan respectively. The better efficiency and effectiveness of the improved NSGA-II are proved by comparisons with other state-of-the-art algorithms. Additionally, a sensitivity analysis is conducted to ascertain the degree of contribution of the model parameters towards the value of objectives, which provides management implications to support the decision-making of seru production for the enterprise.

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ABSTRACT To support shifting to high mix/low volume production, manufacturers in high wage countries aim for robotizing their production operations – with a special focus on the late production phases, where robotic assembly cells are then confronted with any complexities resulting from part and product varieties. The ‘World Robot Challenge 2018’ (WRC 2018) emulated such high mix/low volume production scenarios in a competition taking place in Tokyo, Japan. As part of our activities in SDU's newly founded I4.0 Lab, we integrated and advanced our experiences and developments from our various R & D projects in a novel robotic assembly cell design to compete in the WRC 2018. This article describes the system architecture as well as main aspects of its implementation regarding robot control, robot programming and computer vision and how they contributed to winning the challenge. Due to the application of collaborative robots, the cell design allows for operation without fences. Hence, multiple copies of the cell can be arranged in a highly reconfigurable, highly adaptable matrix structure in which several production flows can be handled concurrently. This concept was demonstrated by the installation of a duplicate cell that allowed for parallel developments on two cells and prolonged development also after shipping the first cell to Japan. GRAPHICAL ABSTRACT

This study aims to design an innovative method to evaluate the effects of line-seru conversion in terms of various environmental and economic performance criteria. For this purpose, an integrated approach using simulation and data envelopment analysis (DEA) assisted with various performance criteria is proposed for the first time. The simulation studies involve diverse scenarios for the production of a circuit breaker on assembly lines balanced with RPW and COMSOAL heuristic algorithms, and seru production systems. The simulation results indicate a significant increase in labour effectiveness and station/yatai utilization ratios, complete removal of WIP inventories, which led to a growth in productivity up to 43.29%. The number of workers, carbon-dioxide emission, required work-space, training costs of workers and equipment cost data were incorporated in the results to evaluate the applicability and relative efficiency of the developed scenarios by use of the CCR and BCC models of DEA. The calculated scale efficiency shows that the scenarios for assembly lines balanced with COMSOAL and the seru-based scenarios were both fully effective. The seru production system scenario with 6 yatais was the most effective scenario due to reduced task times after conversion. Also, line-seru conversion proved to be advantageous despite the high training costs.

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The development of resilience in manufacturing systems has drawn more attention than ever. Using redundant components is one of the key strategies for building and enhancing the resilience of a manufacturing system. However, current redundancy strategies require duplicated machinery employed either in active or in standby status. This in turn causes extra costs in designing and achieving resilience. Achieving an efficient deployment of the redundant component in the face of failures is also challenging. In this paper, we introduce a novel redundancy strategy, called adaptive standby redundancy (ASR), to achieve resilient performance for discrete manufacturing systems while reducing the cost of employing the duplicated components that are typically used in traditional systems. This novel strategy permits achievement of high levels of utilisation of the system and graceful degradation in case of failure, keeping the system functional. The strategy is then validated in a developed robotic flexible assembly cell (RFAC), which is tested and results on its efficacy and performance enhancement are discussed.

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The Virtual CommissioningVCtechnology is the latest trend in automotive assembly which, among other benefits, promises a more efficient handling of the complexity in assembly systems, a great reduction in the system's ramp-up time, and a resulting shortening of the product's time to market. This paper presents the application of VC techniques to the case of an industrial robotic cell, involving cooperating robots. The complete workflow of the virtual validation of the cell is presented, and the implementation requirements are discussed. Based on the findings, the outlook and challenges for the wide-range adoption of VC technologies in large-scale assembly systems are provided.

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.

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.

Proper disassembly operations organization and workstation assignment can help increase the efficiency of disassembly systems that are critical for recycling and remanufacturing of end-of-life (EOL) products. A parallel disassembly system layout allows diversification of disassembly tasks and increases flexibility. In this work, a parallel disassembly balancing model considering hiring workers with government benefits (WGB) is established. To quickly find an optimal solution to the model, a salp swarm algorithm (SSA) with a new encoding and decoding process is developed. Moreover, we use the well-known mathematical optimization technique CPLEX to verify the correctness of the proposed model and use a genetic algorithm (GA), a constrained decomposition approach with grids’ optimization (CDG), and a random search (RS) algorithm to show the effectiveness of the proposed algorithm. Experimental results show that the proposed algorithm can perform well on the proposed problem, which is conducive to the society accepting more WGB into the workplace.

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Electronics contract manufacturing has multiple challenges, like high product mix, fluctuating demand, and tight resource constraints. These conditions make workforce planning a critical operational challenge. This study presents a mixedinteger linear programming framework for assigning multi-skilled workers across departments, incorporating role-specific capabilities, productivity differences, and partial allocation of managerial staff. These characteristics are common in contract manufacturing. The incorporation of operational and strategic staffing constraints allows decision makers to optimize the headcount distribution across departments. An illustrative example is provided to demonstrate that the model yields a feasible solution. The framework lays the foundations for a real-world case study and future research into indirect labor estimation, demanddriven productivity modeling, and dynamic workforce reallocation.

To solve the scheduling problem of multi-skilled workers in uncertain environments, a model to minimize production costs and delays is created. An improved Sparrow Search Algorithm is designed to solve this model (GWHASSA). The algorithm introduces a transformation mechanism and uses position update strategies to improve local search. The algorithm is tested through real cases for effectiveness and feasibility.

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