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A new immune multi-agent system for the flexible job shop scheduling problem

Author

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  • Wei Xiong

    (University of Science and Technology Beijing
    North China University of Science and Technology)

  • Dongmei Fu

    (University of Science and Technology Beijing)

Abstract

Scheduling for the flexible job shop is very important and challenging in manufacturing field. Multi-agent-based approaches have been used to solve the flexible job shop scheduling problem (FJSP), in order to reduce complexity and cost, increase flexibility, and enhance robustness. However, the quality of solution obtained by the multi-agent approach is always worse than the centralized meta-heuristic algorithms. The immune system is a distributed and complicated information processing system, which can protect body from foreign antigens by immune responses. In this paper, we analyze the similarities between the FJSP and humoral immunity, which is one of the immune responses. Based on the similarities, we develop a new immune multi-agent scheduling system (NIMASS) to solve the FJSP with the objective of minimizing the maximal completion time (makespan). In order to acquire the higher-quality solution of the FJSP, we simulate humoral immunity to establish the architecture of NIMASS and the negotiation strategies of NIMASS, which are proposed for negotiation among agents. NIMASS was tested on different benchmark instances of the FJSP. In comparison with the multi-agent approaches and the centralized heuristic algorithms, the computational results indicate that NIMASS can effectively improve the quality of solution in very short time. And the computational time of NIMASS is superior to that of the centralized meta-heuristic algorithms, especially for the complex FJSPs. These results indicate that NIMASS can be very useful in applications that deal with real-time FJSPs.

Suggested Citation

  • Wei Xiong & Dongmei Fu, 2018. "A new immune multi-agent system for the flexible job shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 857-873, April.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:4:d:10.1007_s10845-015-1137-2
    DOI: 10.1007/s10845-015-1137-2
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    References listed on IDEAS

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    1. 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.
    2. Martin J. Osborne & Ariel Rubinstein, 1994. "A Course in Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262650401, April.
    3. Egon Balas, 1969. "Machine Sequencing Via Disjunctive Graphs: An Implicit Enumeration Algorithm," Operations Research, INFORMS, vol. 17(6), pages 941-957, December.
    4. Kacem, Imed & Hammadi, Slim & Borne, Pierre, 2002. "Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 60(3), pages 245-276.
    5. M. R. Garey & D. S. Johnson & Ravi Sethi, 1976. "The Complexity of Flowshop and Jobshop Scheduling," Mathematics of Operations Research, INFORMS, vol. 1(2), pages 117-129, May.
    6. Moslehi, Ghasem & Mahnam, Mehdi, 2011. "A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search," International Journal of Production Economics, Elsevier, vol. 129(1), pages 14-22, January.
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    Cited by:

    1. Olumide Emmanuel Oluyisola & Swapnil Bhalla & Fabio Sgarbossa & Jan Ola Strandhagen, 2022. "Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 311-332, January.
    2. Tsegay Tesfay Mezgebe & Hind Bril El Haouzi & Guillaume Demesure & Remi Pannequin & Andre Thomas, 2020. "Multi-agent systems negotiation to deal with dynamic scheduling in disturbed industrial context," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1367-1382, August.
    3. Yingli Li & Jiahai Wang & Zhengwei Liu, 2022. "A simple two-agent system for multi-objective flexible job-shop scheduling," Journal of Combinatorial Optimization, Springer, vol. 43(1), pages 42-64, January.
    4. Anran Zhao & Peng Liu & Xiyu Gao & Guotai Huang & Xiuguang Yang & Yuan Ma & Zheyu Xie & Yunfeng Li, 2022. "Data-Mining-Based Real-Time Optimization of the Job Shop Scheduling Problem," Mathematics, MDPI, vol. 10(23), pages 1-30, December.
    5. Mohd. Shaaban Hussain & Mohammed Ali, 2019. "A Multi-agent Based Dynamic Scheduling of Flexible Manufacturing Systems," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 20(3), pages 267-290, September.
    6. Fei Luan & Zongyan Cai & Shuqiang Wu & Shi Qiang Liu & Yixin He, 2019. "Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm," Mathematics, MDPI, vol. 7(8), pages 1-17, August.
    7. Didden, Jeroen B.H.C. & Dang, Quang-Vinh & Adan, Ivo J.B.F., 2024. "Enhancing stability and robustness in online machine shop scheduling: A multi-agent system and negotiation-based approach for handling machine downtime in industry 4.0," European Journal of Operational Research, Elsevier, vol. 316(2), pages 569-583.
    8. Xuan Jing & Xifan Yao & Min Liu & Jiajun Zhou, 2024. "Multi-agent reinforcement learning based on graph convolutional network for flexible job shop scheduling," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 75-93, January.

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