Improved genetic-simulated annealing algorithm for <i>seru</i> loading problem with downward substitution under stochastic environment

Zhang, Zhe; Wang, Lili; Song, Xiaoling; Huang, Huijun; Yin, Yong

doi:10.1080/01605682.2021.1939172

Cited by 21 publications

(3 citation statements)

References 36 publications

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“…Previous studies have viewed rotating serus as a black box, as seen in the works of Gai et al (2022), Liu et al (2022), Zhan et al (2023), Zhang, Song, Gong, et al (2022a, 2022b), Zhang, Wang, Song, et al (2022), Zhang, Song, Huang, et al (2022), and Li et al (2023). This study is the first to open the black box by analyzing the inside mechanisms of rotating serus .…”

Section: Discussionmentioning

confidence: 99%

Maximizing the throughput of a rotating Seru with nonpreemptive discrete stations

Yin

et al. 2023

Naval Research Logistics

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Seru production systems are an effective way to respond to ever‐changing market demand. This article focuses on maximizing the throughput of rotating serus with nonpreemptive stations, where a worker's operations cannot be disrupted. We analyze the effects of unbalanced worker velocities on non‐value‐added idle times. Through the use of dynamical system theories, we explicate the mechanism and dynamics of rotating serus, and identify the rules used to coordinate workers and distribute work content among stations to achieve the highest throughput. These findings provide practical guidelines for managers in floor shops for optimizing rotating seru design and maximizing throughput. Additionally, we explore the chaotic characteristics of rotating serus and simulate the effect of various factors on throughput. Finally, our comparative analysis demonstrates that the rotating seru offers a viable alternative to existing production systems to adapt to fluctuating demand.

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Section: Discussionmentioning

confidence: 99%

Maximizing the throughput of a rotating Seru with nonpreemptive discrete stations

Yin

et al. 2023

Naval Research Logistics

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“…Ayough et al (2020) proposed an efficient invasive weed optimization algorithm to solve the job rotation scheduling and line-Seru conversion. In addition, a SA genetic algorithm (Luo, 2021), an improved genetic SA algorithm Zhang et al (2021b) and an NSGA II-based modal algorithm (Liu et al , 2021b) are used for the Seru loading problem. A generalized exact solution method and an automatic heuristic design method (Zhan et al , 2021) are used for the Seru scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

Assembly line-Seru conversion in the C2M enterprise: an empirical study in China

Shan

2022

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Purpose To respond to customer needs and achieve customized manufacturing, the manufacturing industry, as represented by electronics assembly companies, has embarked on a path of business model transformation (customer to manufacturer [C2M]). The purpose of this paper is to examine the practical application of assembly line-Seru conversion in a Chinese electronics assembly company during the C2M transition. Design/methodology/approach To begin with, this paper proposed a production line improvement scheme suitable for the conversion of C2M manufacturing enterprise assembly line-Seru based on an analysis of the difficulties encountered in the existing production line of A company in China. Then, a mathematical model was presented for the minimum value of the makespan and the maximum workers’ expenditure between Serus. Finally, the SA-NSGA-II algorithm and the entropy-weight TOPSIS approach were used to determine the optimal scheme for Seru unit, batch, product type and worker distribution. Findings Seru production and multiskilled workers are more suited to the C2M business model. The most effective strategy for worker allocation can reduce the number of employees and makespan in Serus. Additionally, the performance of the SA-NSGA-II algorithm and the method of selecting the optimal solution from the Pareto solution by the entropy-weighted TOPSIS method is also demonstrated. Practical implications Through a detailed study of how to transform the production line, other companies can apply the methods outlined in this article to shorten the delivery time, make full use of the abilities of workers and assign workers to specific positions, thereby reducing the number of workers, workers’ expenditure and improving the balance rate of production lines. Originality/value Given the scarcity of studies on the production method of C2M-type firms in the prior literature, this paper examined the assembly line-Seru conversion problem with the goal of minimizing the makespan and worker expenditure. To address the NSGA-II algorithm’s insufficient convergence, the simulated annealing process is incorporated into the method, which improves the optimization performance.

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“…Zhang et al (2021a) concentrated on workers have different processing time for different processes, studied the optimal worker assignment problem to minimize the total risk. Zhang et al (2021b) studied how allocate workers and products to seru units, constructed a combinatorial optimization seru loading model to maximize system profits and designed an improved genetic-simulated annealing algorithm (IGSA) to obtain optimal loading solutions. Unfortunately, due to the complexity of multi-skilled, the seru loading studies above, all workers are assumed to be full-skilled.…”

Section: Introductionmentioning

confidence: 99%

Multi-skilled worker assignment in seru production system for the trade-off between production efficiency and workload fairness

Zeng

2022

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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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Improved genetic-simulated annealing algorithm for seru loading problem with downward substitution under stochastic environment

Cited by 21 publications

References 36 publications

Maximizing the throughput of a rotating Seru with nonpreemptive discrete stations

Maximizing the throughput of a rotating Seru with nonpreemptive discrete stations

Assembly line-Seru conversion in the C2M enterprise: an empirical study in China

Multi-skilled worker assignment in seru production system for the trade-off between production efficiency and workload fairness

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