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A Robust Single-Machine Scheduling Problem with Two Job Parameter Scenarios

Author

Listed:
  • Gang Xuan

    (School of Administration Business, Zhejiang University of Finance and Economics Dongfang College, Haining 314408, China)

  • Win-Chin Lin

    (Department of Statistics, Feng Chia University, Taichung City 40724, Taiwan)

  • Shuenn-Ren Cheng

    (Department of E-Sport Technology Management, Cheng Shiu University, Kaohsiung City 83347, Taiwan)

  • Wei-Lun Shen

    (Department of Statistics, Feng Chia University, Taichung City 40724, Taiwan)

  • Po-An Pan

    (Department of Statistics, Feng Chia University, Taichung City 40724, Taiwan)

  • Chih-Ling Kuo

    (Department of Food and Beverage Management, Cheng Shiu University, Kaohsiung City 83347, Taiwan)

  • Chin-Chia Wu

    (Department of Statistics, Feng Chia University, Taichung City 40724, Taiwan)

Abstract

In many real-world environments, machine breakdowns or worker performance instabilities cause uncertainty in job processing times, while working environment changes or transportation delays will postpone finished production for customers. The factors that impact the task processing times and/or deadlines vary. In view of the uncertainty, job processing times and/or job due dates cannot be fixed numbers. Inspired by this fact, we introduce a scenario-dependent processing time and due date concept into a single-machine environment. The measurement minimizes the total job tardiness in the worst case. The same problem without the presence of processing time uncertainty has been an NP-hard problem. First, to solve this difficult model, an exact method, including a lower bound and some dominance properties, is proposed. Next, three scenario-dependent heuristic algorithms are proposed. Additionally, a population-based iterated greedy algorithm is proposed in the hope of increasing the diversity of the solutions. The results of all related algorithms are determined and compared using the appropriate statistical tools.

Suggested Citation

  • Gang Xuan & Win-Chin Lin & Shuenn-Ren Cheng & Wei-Lun Shen & Po-An Pan & Chih-Ling Kuo & Chin-Chia Wu, 2022. "A Robust Single-Machine Scheduling Problem with Two Job Parameter Scenarios," Mathematics, MDPI, vol. 10(13), pages 1-17, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2176-:d:845099
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    References listed on IDEAS

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    1. Framinan, Jose M. & Perez-Gonzalez, Paz, 2018. "Order scheduling with tardiness objective: Improved approximate solutions," European Journal of Operational Research, Elsevier, vol. 266(3), pages 840-850.
    2. HazIr, Öncü & Haouari, Mohamed & Erel, Erdal, 2010. "Robust scheduling and robustness measures for the discrete time/cost trade-off problem," European Journal of Operational Research, Elsevier, vol. 207(2), pages 633-643, December.
    3. Shi, Yong & Boudouh, Toufik & Grunder, Olivier, 2019. "A robust optimization for a home health care routing and scheduling problem with consideration of uncertain travel and service times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 52-95.
    4. Ruiz, Ruben & Stutzle, Thomas, 2007. "A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 177(3), pages 2033-2049, March.
    5. Quang Chieu Ta & Jean-Charles Billaut & Jean-Louis Bouquard, 2018. "Matheuristic algorithms for minimizing total tardiness in the m-machine flow-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 617-628, March.
    6. Jian Yang & Gang Yu, 2002. "On the Robust Single Machine Scheduling Problem," Journal of Combinatorial Optimization, Springer, vol. 6(1), pages 17-33, March.
    7. Sen, Tapan & Sulek, Joanne M. & Dileepan, Parthasarati, 2003. "Static scheduling research to minimize weighted and unweighted tardiness: A state-of-the-art survey," International Journal of Production Economics, Elsevier, vol. 83(1), pages 1-12, January.
    8. Koulamas, Christos, 2010. "The single-machine total tardiness scheduling problem: Review and extensions," European Journal of Operational Research, Elsevier, vol. 202(1), pages 1-7, April.
    9. 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.
    10. 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.
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    Cited by:

    1. Lung-Yu Li & Jian-You Xu & Shuenn-Ren Cheng & Xingong Zhang & Win-Chin Lin & Jia-Cheng Lin & Zong-Lin Wu & Chin-Chia Wu, 2022. "A Genetic Hyper-Heuristic for an Order Scheduling Problem with Two Scenario-Dependent Parameters in a Parallel-Machine Environment," Mathematics, MDPI, vol. 10(21), pages 1-22, November.

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