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An Improved Mayfly Method to Solve Distributed Flexible Job Shop Scheduling Problem under Dual Resource Constraints

Author

Listed:
  • Shoujing Zhang

    (Department of Industrial Engineering, Xi’an Key Laboratory of Modern Intelligent Textile Equipment, Xi’an Polytechnic University, Xi’an 710600, China)

  • Tiantian Hou

    (Department of Industrial Engineering, Xi’an Key Laboratory of Modern Intelligent Textile Equipment, Xi’an Polytechnic University, Xi’an 710600, China)

  • Qing Qu

    (Department of Industrial Engineering, Xi’an Key Laboratory of Modern Intelligent Textile Equipment, Xi’an Polytechnic University, Xi’an 710600, China)

  • Adam Glowacz

    (Department of Automatic, Control and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland)

  • Samar M. Alqhtani

    (Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia)

  • Muhammad Irfan

    (Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi Arabia)

  • Grzegorz Królczyk

    (Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland)

  • Zhixiong Li

    (Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland
    Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea)

Abstract

Aiming at the distributed flexible job shop scheduling problem under dual resource constraints considering the influence of workpiece transportation time between factories and machines, a distributed flexible job shop scheduling problem (DFJSP) model with the optimization goal of minimizing completion time is established, and an improved mayfly algorithm (IMA) is proposed to solve it. Firstly, the mayfly position vector is discrete mapped to make it applicable to the scheduling problem. Secondly, three-layer coding rules of process, worker, and machine is adopted, in which the factory selection is reflected by machine number according to the characteristics of the model, and a hybrid initialization strategy is designed to improve the population quality and diversity. Thirdly, an active time window decoding strategy considering transportation time is designed for the worker–machine idle time window to improve the local optimization performance of the algorithm. In addition, the improved crossover and mutation operators is designed to expand the global search range of the algorithm. Finally, through simulation experiments, the results of various algorithms are compared to verify the effectiveness of the proposed algorithm for isomorphism and isomerism factories instances.

Suggested Citation

  • Shoujing Zhang & Tiantian Hou & Qing Qu & Adam Glowacz & Samar M. Alqhtani & Muhammad Irfan & Grzegorz Królczyk & Zhixiong Li, 2022. "An Improved Mayfly Method to Solve Distributed Flexible Job Shop Scheduling Problem under Dual Resource Constraints," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12120-:d:924739
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    References listed on IDEAS

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    1. Guiliang Gong & Raymond Chiong & Qianwang Deng & Xuran Gong, 2020. "A hybrid artificial bee colony algorithm for flexible job shop scheduling with worker flexibility," International Journal of Production Research, Taylor & Francis Journals, vol. 58(14), pages 4406-4420, July.
    2. De Giovanni, L. & Pezzella, F., 2010. "An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem," European Journal of Operational Research, Elsevier, vol. 200(2), pages 395-408, January.
    3. Xiao-long Zheng & Ling Wang, 2016. "A knowledge-guided fruit fly optimization algorithm for dual resource constrained flexible job-shop scheduling problem," International Journal of Production Research, Taylor & Francis Journals, vol. 54(18), pages 5554-5566, September.
    4. Guiliang Gong & Raymond Chiong & Qianwang Deng & Qiang Luo, 2020. "A memetic algorithm for multi-objective distributed production scheduling: minimizing the makespan and total energy consumption," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1443-1466, August.
    5. Liping Zhou & Zhibin Jiang & Na Geng & Yimeng Niu & Feng Cui & Kefei Liu & Nanshan Qi, 2022. "Production and operations management for intelligent manufacturing: a systematic literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 60(2), pages 808-846, January.
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    Cited by:

    1. Bin Ji & Shujing Zhang & Samson S. Yu & Binqiao Zhang, 2023. "Mathematical Modeling and A Novel Heuristic Method for Flexible Job-Shop Batch Scheduling Problem with Incompatible Jobs," Sustainability, MDPI, vol. 15(3), pages 1-26, January.

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