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Constraint Propagation and Problem Decomposition: A Preprocessing Procedure for the Job Shop Problem

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  • Ulrich Dorndorf
  • Erwin Pesch
  • Toàn Phan-Huy

Abstract

In recent years, constraint propagation techniques have been shown to be highly effective for solving difficult scheduling problems. In this paper, we present an algorithm which combines constraint propagation with a problem decomposition approach in order to simplify the solution of the job shop scheduling problem. This is mainly guided by the observation that constraint propagation is more effective for ‘small’ problem instances. Roughly speaking, the algorithm consists of deducing operation sequences that are likely to occur in an optimal solution of the job shop scheduling problem (JSP). The algorithm for which the name edge-guessing procedure has been chosen – since with respect to the job shop scheduling problem (JSP) the deduction of machine sequences is mainly equivalent to orienting edges in a disjunctive graph – can be applied in a preprocessing step, reducing the solution space, thus speeding up the overall solution process. In spite of the heuristic nature of edge-guessing, it still leads to near-optimal solutions. If combined with a heuristic algorithm, we will demonstrate that given the same amount of computation time, the additional application of edge-guessing leads to better solutions. This has been tested on a set of well-known JSP benchmark problem instances. Copyright Kluwer Academic Publishers 2002

Suggested Citation

  • Ulrich Dorndorf & Erwin Pesch & Toàn Phan-Huy, 2002. "Constraint Propagation and Problem Decomposition: A Preprocessing Procedure for the Job Shop Problem," Annals of Operations Research, Springer, vol. 115(1), pages 125-145, September.
    • Handle: RePEc:spr:annopr:v:115:y:2002:i:1:p:125-145:10.1023/a:1021197120431
    DOI: 10.1023/A:1021197120431
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    Cited by:

    1. Carlos Mencía & María Sierra & Ramiro Varela, 2013. "Depth-first heuristic search for the job shop scheduling problem," Annals of Operations Research, Springer, vol. 206(1), pages 265-296, July.
    2. Zhu, Xuedong & Son, Junbo & Zhang, Xi & Wu, Jianguo, 2023. "Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem," Omega, Elsevier, vol. 117(C).
    3. Kurowski, Krzysztof & Pecyna, Tomasz & Slysz, Mateusz & Różycki, Rafał & Waligóra, Grzegorz & Wȩglarz, Jan, 2023. "Application of quantum approximate optimization algorithm to job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 310(2), pages 518-528.
    4. Dorndorf, Ulrich & Drexl, Andreas & Nikulin, Yury & Pesch, Erwin, 2007. "Flight gate scheduling: State-of-the-art and recent developments," Omega, Elsevier, vol. 35(3), pages 326-334, June.
    5. Dorndorf, Ulrich & Drexl, Andreas & Nikulin, Yury & Pesch, Erwin, 2005. "Flight gate scheduling: State-of-the-art and recent developments," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 584, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    6. Pisut Pongchairerks, 2019. "A Two-Level Metaheuristic Algorithm for the Job-Shop Scheduling Problem," Complexity, Hindawi, vol. 2019, pages 1-11, March.
    7. Müller, David & Müller, Marcus G. & Kress, Dominik & Pesch, Erwin, 2022. "An algorithm selection approach for the flexible job shop scheduling problem: Choosing constraint programming solvers through machine learning," European Journal of Operational Research, Elsevier, vol. 302(3), pages 874-891.
    8. Fatemi-Anaraki, Soroush & Tavakkoli-Moghaddam, Reza & Foumani, Mehdi & Vahedi-Nouri, Behdin, 2023. "Scheduling of Multi-Robot Job Shop Systems in Dynamic Environments: Mixed-Integer Linear Programming and Constraint Programming Approaches," Omega, Elsevier, vol. 115(C).
    9. Giuseppe Lancia & Franca Rinaldi & Paolo Serafini, 2011. "A time-indexed LP-based approach for min-sum job-shop problems," Annals of Operations Research, Springer, vol. 186(1), pages 175-198, June.

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