Research on Flexible Flow-shop batch scheduling based on improved Genetic Algorithm

Zexing Zhu () and Jiahui Chai
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Zexing Zhu: Jiangsu University of Science and Technology, Institute of Economics and Management
Jiahui Chai: Jiangsu University of Science and Technology, Institute of Economics and Management

A chapter in Proceedings of the 2022 4th International Conference on Economic Management and Cultural Industry (ICEMCI 2022), 2023, pp 65-74 from Springer

Abstract: Abstract In this paper, the batch scheduling problem of multi-stage flexible flow shop in mixed-flow production is studied. Considering the factors of parallel machine scheduling and equipment adjustment time, an integrated equal and variable optimal batch strategy is proposed. On this basis, a batch scheduling model is constructed to minimize the completion period, maximum load and total tardiness, and an improved genetic algorithm based on two-layer search framework is designed to solve the problem. In order to obtain a better scheduling scheme, the algorithm adopts three-stage coding and decoding considering adjustment time, and introduces the critical chain method and the minimum critical ratio rule through the integrated iterative process of inner algorithm and outer batch. The example results show that compared with the original scheme, the optimized scheduling scheme can effectively reduce the completion cycle by 10.35% and the order delay ratio by 40%. At the same time, the production line adjustment frequency is reduced by 22.45% compared with the scheme under equal division strategy. According to the above results, the applicability and effectiveness of the optimized batching strategy and the improved algorithm are verified.

Keywords: Flexible flow shop; Optimized batching strategy; Improved genetic algorithm with two-layer frame (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-098-5_9

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DOI: 10.2991/978-94-6463-098-5_9

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