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Flexible job-shop scheduling/rescheduling in dynamic environment: a hybrid MAS/ACO approach

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  • Sicheng Zhang
  • Tak Nam Wong

Abstract

In real-world manufacturing, disruptions are often encountered during the execution of a predetermined schedule, leading to the degradation of its optimality and feasibility. This study presents a hybrid approach for flexible job-shop scheduling/rescheduling problems under dynamic environment. The approach, coined as ‘HMA’ is a combination of multi-agent system (MAS) negotiation and ant colony optimisation (ACO). A fully distributed MAS structure has been constructed to support the solution-finding process by negotiation among the agents. The features of ACO are introduced into the negotiation mechanism in order to improve the performance of the schedule. Experimental studies have been carried out to evaluate the performance of the approach for scheduling and rescheduling under different types of disruptions. Different rescheduling policies are compared and discussed. The results have shown that the proposed approach is a competitive method for flexible job-shop scheduling/rescheduling for both schedule optimality and computation efficiency.

Suggested Citation

  • Sicheng Zhang & Tak Nam Wong, 2017. "Flexible job-shop scheduling/rescheduling in dynamic environment: a hybrid MAS/ACO approach," International Journal of Production Research, Taylor & Francis Journals, vol. 55(11), pages 3173-3196, June.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:11:p:3173-3196
    DOI: 10.1080/00207543.2016.1267414
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    Cited by:

    1. 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.
    2. Dauzère-Pérès, Stéphane & Ding, Junwen & Shen, Liji & Tamssaouet, Karim, 2024. "The flexible job shop scheduling problem: A review," European Journal of Operational Research, Elsevier, vol. 314(2), pages 409-432.

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