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Analysis of a parallel machine scheduling problem with sequence dependent setup times and job availability intervals

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  • Gedik, Ridvan
  • Rainwater, Chase
  • Nachtmann, Heather
  • Pohl, Ed A.

Abstract

In this study, we propose constraint programming (CP) model and logic-based Benders algorithms in order to make the best decisions for scheduling non-identical jobs with availability intervals and sequence dependent setup times on unrelated parallel machines in a fixed planning horizon. In this problem, each job has a profit, cost and must be assigned to at most one machine in such a way that total profit is maximized. In addition, the total cost has to be less than or equal to a budget level. Computational tests are performed on a real-life case study prepared in collaboration with the U.S. Army Corps of Engineers (USACE). Our initial investigations show that the pure CP model is very efficient in obtaining good quality feasible solutions but, fails to report the optimal solution for the majority of the problem instances. On the other hand, the two logic-based Benders decomposition algorithms are able to obtain near optimal solutions for 86 instances out of 90 examinees. For the remaining instances, they provide a feasible solution. Further investigations show the high quality of the solutions obtained by the pure CP model.

Suggested Citation

  • Gedik, Ridvan & Rainwater, Chase & Nachtmann, Heather & Pohl, Ed A., 2016. "Analysis of a parallel machine scheduling problem with sequence dependent setup times and job availability intervals," European Journal of Operational Research, Elsevier, vol. 251(2), pages 640-650.
    • Handle: RePEc:eee:ejores:v:251:y:2016:i:2:p:640-650
    DOI: 10.1016/j.ejor.2015.11.020
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    Cited by:

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    3. Nascimento, Paulo Jorge & Silva, Cristóvão & Antunes, Carlos Henggeler & Moniz, Samuel, 2024. "Optimal decomposition approach for solving large nesting and scheduling problems of additive manufacturing systems," European Journal of Operational Research, Elsevier, vol. 317(1), pages 92-110.
    4. Boccia, Maurizio & Masone, Adriano & Sterle, Claudio & Murino, Teresa, 2023. "The parallel AGV scheduling problem with battery constraints: A new formulation and a matheuristic approach," European Journal of Operational Research, Elsevier, vol. 307(2), pages 590-603.
    5. Xiong, Hegen & Fan, Huali & Jiang, Guozhang & Li, Gongfa, 2017. "A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints," European Journal of Operational Research, Elsevier, vol. 257(1), pages 13-24.
    6. Emine Akyol Ozer & Tugba Sarac, 2019. "MIP models and a matheuristic algorithm for an identical parallel machine scheduling problem under multiple copies of shared resources constraints," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 94-124, April.
    7. Allahverdi, Ali & Aydilek, Harun & Aydilek, Asiye, 2018. "No-wait flowshop scheduling problem with two criteria; total tardiness and makespan," European Journal of Operational Research, Elsevier, vol. 269(2), pages 590-601.
    8. Enayaty-Ahangar, Forough & Rainwater, Chase E. & Sharkey, Thomas C., 2019. "A Logic-based Decomposition Approach for Multi-Period Network Interdiction Models," Omega, Elsevier, vol. 87(C), pages 71-85.
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    10. Giorgi Tadumadze & Simon Emde & Heiko Diefenbach, 2020. "Exact and heuristic algorithms for scheduling jobs with time windows on unrelated parallel machines," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(2), pages 461-497, June.

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