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Bi-objective flexible job shop scheduling on machines considering condition-based maintenance activities

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  • Liwei Li
  • Lei Deng
  • Baoping Tang
  • Fuqi Wang

Abstract

This paper investigates multi-objective flexible job shop scheduling considering maintenance activities. The condition-based maintenance strategy is used to reduce machines breakdown. After maintenance activities are completed, the machine degradation model’s parameters are updated using Bayesian inference to make it more realistic. A novel multi-objective evolutionary algorithm is designed to address the multi-criteria scheduling problem. An innovative insertion algorithm is proposed in this paper to balance production plan and maintenance activities. According to the experimental results, the designed evolutionary algorithm performs better than the other two traditional algorithms, and the insertion approach can reduce the impact of maintenance activities on production plan by up to 200%.

Suggested Citation

  • Liwei Li & Lei Deng & Baoping Tang & Fuqi Wang, 2024. "Bi-objective flexible job shop scheduling on machines considering condition-based maintenance activities," Journal of Risk and Reliability, , vol. 238(6), pages 1244-1255, December.
    • Handle: RePEc:sae:risrel:v:238:y:2024:i:6:p:1244-1255
    DOI: 10.1177/1748006X231205185
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    References listed on IDEAS

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    1. Stéphane Dauzère-Pérès & Jan Paulli, 1997. "An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search," Annals of Operations Research, Springer, vol. 70(0), pages 281-306, April.
    2. Li, Xinyu & Gao, Liang, 2016. "An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 174(C), pages 93-110.
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