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Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns

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  • Xiong, Jian
  • Xing, Li-ning
  • Chen, Ying-wu

Abstract

This study addresses robust scheduling for a flexible job-shop scheduling problem with random machine breakdowns. Two objectives – makespan and robustness – are simultaneously considered. Robustness is indicated by the expected value of the relative difference between the deterministic and actual makespan. Utilizing the available information about machine breakdowns, two surrogate measures for robustness are developed. Specifically, the first suggested surrogate measure considers the probability of machine breakdowns, while the second surrogate measure considers the location of float times and machine breakdowns. To address this problem, a multi-objective evolutionary algorithm is presented in this paper. The experimental results indicate that, compared with several other existing surrogate measures, the first suggested surrogate measure performs better for small cases, while the second surrogate measure performs better for both small and relatively large cases.

Suggested Citation

  • Xiong, Jian & Xing, Li-ning & Chen, Ying-wu, 2013. "Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns," International Journal of Production Economics, Elsevier, vol. 141(1), pages 112-126.
    • Handle: RePEc:eee:proeco:v:141:y:2013:i:1:p:112-126
    DOI: 10.1016/j.ijpe.2012.04.015
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    3. Abdelhamid Boudjelida, 2019. "On the robustness of joint production and maintenance scheduling in presence of uncertainties," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1515-1530, April.
    4. Shichang Xiao & Zigao Wu & Hongyan Dui, 2022. "Resilience-Based Surrogate Robustness Measure and Optimization Method for Robust Job-Shop Scheduling," Mathematics, MDPI, vol. 10(21), pages 1-22, October.
    5. Xiong, Jian & Leus, Roel & Yang, Zhenyu & Abbass, Hussein A., 2016. "Evolutionary multi-objective resource allocation and scheduling in the Chinese navigation satellite system project," European Journal of Operational Research, Elsevier, vol. 251(2), pages 662-675.
    6. Shichang Xiao & Shudong Sun & Jionghua (Judy) Jin, 2017. "Surrogate Measures for the Robust Scheduling of Stochastic Job Shop Scheduling Problems," Energies, MDPI, vol. 10(4), pages 1-26, April.
    7. Jain, S. & Foley, W.J., 2016. "Dispatching strategies for managing uncertainties in automated manufacturing systems," European Journal of Operational Research, Elsevier, vol. 248(1), pages 328-341.
    8. Zigao Wu & Shaohua Yu & Tiancheng Li, 2019. "A Meta-Model-Based Multi-Objective Evolutionary Approach to Robust Job Shop Scheduling," Mathematics, MDPI, vol. 7(6), pages 1-19, June.
    9. Ferreira, Cristiane & Figueira, Gonçalo & Amorim, Pedro, 2022. "Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning," Omega, Elsevier, vol. 111(C).
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    11. Berti, Nicola & Finco, Serena & Battaïa, Olga & Delorme, Xavier, 2021. "Ageing workforce effects in Dual-Resource Constrained job-shop scheduling," International Journal of Production Economics, Elsevier, vol. 237(C).
    12. Alejandro Vital-Soto & Mohammed Fazle Baki & Ahmed Azab, 2023. "A multi-objective mathematical model and evolutionary algorithm for the dual-resource flexible job-shop scheduling problem with sequencing flexibility," Flexible Services and Manufacturing Journal, Springer, vol. 35(3), pages 626-668, September.
    13. 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.
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