An Automatic Heuristic Design Approach for Seru Scheduling Problem with Resource Conflicts

Zhan, Rongxin; Zhang, Jinhui; Cui, Zihua; Peng, Jin; Li, Dongni

doi:10.1155/2021/8166343

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…Seru production has been studied by several scholars. They specialized in reducing makespan, total labor hours, training cost, and manpower (Liu et al, 2013(Liu et al, , 2021a(Liu et al, , 2021bSun et al, 2020;Ying and Tsai, 2017;Yu et al, 2012;Zhan et al, 2021). Stecke et al (2012) described the history of seru and defined the various types of seru.…”

Section: Introductionmentioning

confidence: 99%

To reduce maximum tardiness by Seru Production: model, cooperative algorithm combining reinforcement learning and insights

Fu¹,

Yu²,

Sun³

et al. 2023

10.5267/j.ijiec

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The maximum tardiness reflects the worst level of service associated with customer needs; thus, the principle that seru production reduces the maximum tardiness is investigated, and a model to minimize the maximum tardiness of the seru production system is established. In order to obtain the exact solution, the non-linear seru production model with minimizing the maximum tardiness is split into a seru formation model and a linear seru scheduling model. We propose an efficient cooperative algorithm using a genetic algorithm and an innovative reinforcement learning algorithm (CAGARL) for large-scale problems. Specifically, the GA is designed for the seru formation problem. Moreover, the QL-seru algorithm (QLSA) is designed for the seru scheduling problem by combining the features of meta-heuristics and reinforcement learning. In the QLSA, we design an innovative QL-seru table and two state trimming rules to save computational time. After extensive experiments, compared with the previous algorithm, CAGARL improved by an average of 56.6%. Finally, several managerial insights on reducing maximum tardiness are proposed.

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Section: Introductionmentioning

confidence: 99%

To reduce maximum tardiness by Seru Production: model, cooperative algorithm combining reinforcement learning and insights

Fu¹,

Yu²,

Sun³

et al. 2023

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…Additionally, seru scheduling is the key to reflecting the efficiency and flexibility of seru systems, and whether the advantages of seru systems can be fully utilized mainly depends on the core step of seru scheduling. In terms of seru scheduling, Zhan et al [9] proposed the GP-SS scheduling rule to address the seru scheduling problem with resource conflicts. Jiang et al [10] transformed the seru scheduling problem into an assignment problem.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Production Schedules: Balancing Worker Cooperation and Learning Dynamics in Seru Systems

Liu,

Dai,

Wang

2023

Processes

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This paper aims to investigate the seru scheduling problem while considering the dual effects of worker cooperation and learning behavior to minimize the makespan and order processing time. Given the complexity of this research problem, an improved shuffled frog leaping algorithm based on a genetic algorithm is proposed. We design a double-layer encoding based on the problem, introduce a single point and uniform crossover operator, and select the crossover method in probability form to complete the evolution of the meme group. To avoid damaging grouping information, the individual encoding structure is transformed into unit form. Finally, numerical experiments were conducted using numerical examples of large and small sizes for verification. The experimental results demonstrate the feasibility of the proposed model and algorithm, as well as the necessity of considering worker dual behavior in the seru scheduling problem.

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“…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. Zhang et al (2022) considered the sequence-dependent setup time, DeJong’s learning effect and job splitting (Wang et al , 2021) while solving Seru scheduling problems.…”

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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An Automatic Heuristic Design Approach for Seru Scheduling Problem with Resource Conflicts

Cited by 5 publications

References 39 publications

To reduce maximum tardiness by Seru Production: model, cooperative algorithm combining reinforcement learning and insights

To reduce maximum tardiness by Seru Production: model, cooperative algorithm combining reinforcement learning and insights

Optimizing Production Schedules: Balancing Worker Cooperation and Learning Dynamics in Seru Systems

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

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