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Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm

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  • Al-Hinai, Nasr
  • ElMekkawy, T.Y.

Abstract

This paper addresses the problem of finding robust and stable solutions for the flexible job shop scheduling problem with random machine breakdowns. A number of bi-objective measures combining the robustness and stability of the predicted schedule are defined and compared while using the same rescheduling method. Consequently, a two-stage Hybrid Genetic Algorithm (HGA) is proposed to generate the predictive schedule. The first stage optimizes the primary objective, minimizing makespan in this work, where all the data is considered to be deterministic with no expected disruptions. The second stage optimizes the bi-objective function and integrates machines assignments and operations sequencing with the expected machine breakdown in the decoding space. An experimental study and Analysis of Variance (ANOVA) is conducted to study the effect of different proposed measures on the performance of the obtained results. Results indicate that different measures have different significant effects on the relative performance of the proposed method. Furthermore, the effectiveness of the current proposed method is compared against three other methods; two are taken from literature and the third is a combination of the former two methods.

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  • Al-Hinai, Nasr & ElMekkawy, T.Y., 2011. "Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm," International Journal of Production Economics, Elsevier, vol. 132(2), pages 279-291, August.
    • Handle: RePEc:eee:proeco:v:132:y:2011:i:2:p:279-291
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    1. Laslo, Zohar & Golenko-Ginzburg, Dimitri & Keren, Baruch, 2008. "Optimal booking of machines in a virtual job-shop with stochastic processing times to minimize total machine rental and job tardiness costs," International Journal of Production Economics, Elsevier, vol. 111(2), pages 812-821, February.
    2. Artigues, Christian & Michelon, Philippe & Reusser, Stephane, 2003. "Insertion techniques for static and dynamic resource-constrained project scheduling," European Journal of Operational Research, Elsevier, vol. 149(2), pages 249-267, September.
    3. Lambrechts, Olivier & Demeulemeester, Erik & Herroelen, Willy, 2008. "A tabu search procedure for developing robust predictive project schedules," International Journal of Production Economics, Elsevier, vol. 111(2), pages 493-508, February.
    4. Surico, M. & Kaymak, U. & Naso, D. & Dekker, R., 2006. "Hybrid Meta-Heuristics for Robust Scheduling," ERIM Report Series Research in Management ERS-2006-018-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    5. Mula, J. & Poler, R. & Garcia-Sabater, J.P. & Lario, F.C., 2006. "Models for production planning under uncertainty: A review," International Journal of Production Economics, Elsevier, vol. 103(1), pages 271-285, September.
    6. Xia, Yu & Chen, Bintong & Yue, Jinfeng, 2008. "Job sequencing and due date assignment in a single machine shop with uncertain processing times," European Journal of Operational Research, Elsevier, vol. 184(1), pages 63-75, January.
    7. 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.
    8. Guo, Bo & Nonaka, Yasuo, 1999. "Rescheduling and optimization of schedules considering machine failures," International Journal of Production Economics, Elsevier, vol. 60(1), pages 503-513, April.
    9. V. Vinod & R. Sridharan, 2009. "Development and analysis of scheduling decision rules for a dynamic flexible job shop production system: a simulation study," International Journal of Business Performance Management, Inderscience Enterprises Ltd, vol. 11(1/2), pages 43-71.
    10. Sergio Cavalieri & Sergio Terzi, 2006. "Proposal of a performance measurement system for the evaluation of scheduling solutions," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 8(1/2/3), pages 248-263.
    11. Herroelen, Willy & Leus, Roel, 2005. "Project scheduling under uncertainty: Survey and research potentials," European Journal of Operational Research, Elsevier, vol. 165(2), pages 289-306, September.
    12. Aytug, Haldun & Lawley, Mark A. & McKay, Kenneth & Mohan, Shantha & Uzsoy, Reha, 2005. "Executing production schedules in the face of uncertainties: A review and some future directions," European Journal of Operational Research, Elsevier, vol. 161(1), pages 86-110, February.
    13. Qi, Xiangtong & Bard, Jonathan F. & Yu, Gang, 2006. "Disruption management for machine scheduling: The case of SPT schedules," International Journal of Production Economics, Elsevier, vol. 103(1), pages 166-184, September.
    14. X. Cai & F. S. Tu, 1996. "Scheduling jobs with random processing times on a single machine subject to stochastic breakdowns to minimize early‐tardy penalties," Naval Research Logistics (NRL), John Wiley & Sons, vol. 43(8), pages 1127-1146, December.
    15. Anglani, Alfredo & Grieco, Antonio & Guerriero, Emanuela & Musmanno, Roberto, 2005. "Robust scheduling of parallel machines with sequence-dependent set-up costs," European Journal of Operational Research, Elsevier, vol. 161(3), pages 704-720, March.
    16. Richard L. Daniels & Panagiotis Kouvelis, 1995. "Robust Scheduling to Hedge Against Processing Time Uncertainty in Single-Stage Production," Management Science, INFORMS, vol. 41(2), pages 363-376, February.
    17. Vinod, V. & Sridharan, R., 2011. "Simulation modeling and analysis of due-date assignment methods and scheduling decision rules in a dynamic job shop production system," International Journal of Production Economics, Elsevier, vol. 129(1), pages 127-146, January.
    18. S. David Wu & Eui-Seok Byeon & Robert H. Storer, 1999. "A Graph-Theoretic Decomposition of the Job Shop Scheduling Problem to Achieve Scheduling Robustness," Operations Research, INFORMS, vol. 47(1), pages 113-124, February.
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    3. 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.
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    5. 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.
    6. Iwona Paprocka & Bożena Skołud, 2017. "A hybrid multi-objective immune algorithm for predictive and reactive scheduling," Journal of Scheduling, Springer, vol. 20(2), pages 165-182, April.
    7. 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.
    8. Jose L. Andrade-Pineda & David Canca & Pedro L. Gonzalez-R & M. Calle, 2020. "Scheduling a dual-resource flexible job shop with makespan and due date-related criteria," Annals of Operations Research, Springer, vol. 291(1), pages 5-35, August.
    9. Yiyi Xu & M’hammed Sahnoun & Fouad Ben Abdelaziz & David Baudry, 2022. "A simulated multi-objective model for flexible job shop transportation scheduling," Annals of Operations Research, Springer, vol. 311(2), pages 899-920, April.
    10. Cheng, T.C.E. & Wu, Chin-Chia & Chen, Juei-Chao & Wu, Wen-Hsiang & Cheng, Shuenn-Ren, 2013. "Two-machine flowshop scheduling with a truncated learning function to minimize the makespan," International Journal of Production Economics, Elsevier, vol. 141(1), pages 79-86.
    11. Constantin Waubert de Puiseau & Richard Meyes & Tobias Meisen, 2022. "On reliability of reinforcement learning based production scheduling systems: a comparative survey," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 911-927, April.
    12. 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.
    13. Che, Ada & Feng, Jianguang & Chen, Haoxun & Chu, Chengbin, 2015. "Robust optimization for the cyclic hoist scheduling problem," European Journal of Operational Research, Elsevier, vol. 240(3), pages 627-636.
    14. 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.
    15. Ning Li & Shuzhao Feng & Tao Lei & Haiwang Ye & Qizhou Wang & Liguan Wang & Mingtao Jia, 2022. "Rescheduling Plan Optimization of Underground Mine Haulage Equipment Based on Random Breakdown Simulation," Sustainability, MDPI, vol. 14(6), pages 1-18, March.
    16. Gabriel Mauricio Zambrano-Rey & Eliana María González-Neira & Gabriel Fernando Forero-Ortiz & María José Ocampo-Monsalve & Andrea Rivera-Torres, 2024. "Minimizing the expected maximum lateness for a job shop subject to stochastic machine breakdowns," Annals of Operations Research, Springer, vol. 338(1), pages 801-833, July.
    17. 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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