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Improved Self-Learning Genetic Algorithm for Solving Flexible Job Shop Scheduling

Author

Listed:
  • Ming Jiang

    (School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China)

  • Haihan Yu

    (School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China)

  • Jiaqing Chen

    (School of Economics and Finance, Xi’an Jiaotong University, Xi’an 710061, China)

Abstract

The flexible job shop scheduling problem (FJSP), one of the core problems in the field of generative manufacturing process planning, has become a hotspot and a challenge in manufacturing production research. In this study, an improved self-learning genetic algorithm is proposed. The single mutation approach of the genetic algorithm was improved, while four mutation operators were designed on the basis of process coding and machine coding; their weights were updated and their selection mutation operators were adjusted according to the performance in the iterative process. Combined with the improved population initialization method and the optimized crossover strategy, the local search capability was enhanced, and the convergence speed was accelerated. The effectiveness and feasibility of the algorithm were verified by testing the benchmark arithmetic examples and numerical experiments.

Suggested Citation

  • Ming Jiang & Haihan Yu & Jiaqing Chen, 2023. "Improved Self-Learning Genetic Algorithm for Solving Flexible Job Shop Scheduling," Mathematics, MDPI, vol. 11(22), pages 1-17, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:22:p:4700-:d:1283713
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    References listed on IDEAS

    as
    1. Muhammad Kamal Amjad & Shahid Ikramullah Butt & Rubeena Kousar & Riaz Ahmad & Mujtaba Hassan Agha & Zhang Faping & Naveed Anjum & Umer Asgher, 2018. "Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-32, February.
    2. Maroua Nouiri & Abdelghani Bekrar & Abderezak Jemai & Smail Niar & Ahmed Chiheb Ammari, 2018. "An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 603-615, March.
    3. Éric D. Taillard, 1994. "Parallel Taboo Search Techniques for the Job Shop Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 108-117, May.
    Full references (including those not matched with items on IDEAS)

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