文章基本信息

标题：A Study on the Optimal Flexible Job-Shop Scheduling with Sequence-Dependent Setup Time Based on a Hybrid Algorithm of Improved Quantum Cat Swarm Optimization
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作者：Song, Haicao ; Liu, Pan
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2022
卷号：14
期号：15
页码：1-16
DOI：10.3390/su14159547
语种：English
出版社：MDPI, Open Access Journal
摘要：Multi-item and small-lot-size production modes lead to frequent setup, which involves significant setup times and has a substantial impact on productivity. In this study, we investigated the optimal flexible job-shop scheduling problem with a sequence-dependent setup time. We built a mathematical model with the optimal objective of minimization of the maximum completion time (makespan). Considering the process sequence, which is influenced by setup time, processing time, and machine load limitations, first, processing machinery is chosen based on machine load and processing time, and then processing tasks are scheduled based on setup time and processing time. An improved quantum cat swarm optimization (QCSO) algorithm is proposed to solve the problem, a quantum coding method is introduced, the quantum bit (Q-bit) and cat swarm algorithm (CSO) are combined, and the cats are iteratively updated by quantum rotation angle position; then, the dynamic mixture ratio (MR) value is selected according to the number of algorithm iterations. The use of this method expands our understanding of space and increases operation efficiency and speed. Finally, the improved QCSO algorithm and parallel genetic algorithm (PGA) are compared through simulation experiments. The results show that the improved QCSO algorithm has better results, and the robustness of the algorithm is improved.
关键词：flexible job-shop scheduling; setup time; makespan; quantum cat swarm optimization algorithm (QCSO)