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A Decomposition-Based Genetic Algorithm for the Resource-Constrained Project-Scheduling Problem

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  • Dieter Debels

    (Faculty of Economics and Business Administration, Ghent University, Ghent, Belgium)

  • Mario Vanhoucke

    (Faculty of Economics and Business Administration, Ghent University, Tweekerkenstraat 2, 9000 Ghent, Belgium)

Abstract

In the last few decades, the resource-constrained project-scheduling problem has become a popular problem type in operations research. However, due to its strongly NP-hard status, the effectiveness of exact optimisation procedures is restricted to relatively small instances. In this paper, we present a new genetic algorithm (GA) for this problem that is able to provide near-optimal heuristic solutions. This GA procedure has been extended by a so-called decomposition-based genetic algorithm (DBGA) that iteratively solves subparts of the project. We present computational experiments on two data sets. The first benchmark set is used to illustrate the performance of both the GA and the DBGA. The second set is used to compare the results with current state-of-the-art heuristics and to show that the procedure is capable of producing consistently good results for challenging problem instances. We illustrate that the GA outperforms all state-of-the-art heuristics and that the DBGA further improves the performance of the GA.

Suggested Citation

  • Dieter Debels & Mario Vanhoucke, 2007. "A Decomposition-Based Genetic Algorithm for the Resource-Constrained Project-Scheduling Problem," Operations Research, INFORMS, vol. 55(3), pages 457-469, June.
    • Handle: RePEc:inm:oropre:v:55:y:2007:i:3:p:457-469
    DOI: 10.1287/opre.1060.0358
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    11. Deblaere, Filip & Demeulemeester, Erik & Herroelen, Willy, 2011. "Proactive policies for the stochastic resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 214(2), pages 308-316, October.
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    20. Changchun Liu & Xi Xiang & Li Zheng, 2020. "Value of information sharing in a multiple producers–distributor supply chain," Annals of Operations Research, Springer, vol. 285(1), pages 121-148, February.
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