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A Decomposition-Based Heuristic For The Resource-Constrained Project Scheduling Problem

Author

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  • D. DEBELS
  • M. VANHOUCKE
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, able to provide near-optimal heuristic solutions. This GA procedure has been extended by a so-called decomposition-based heuristic (DBH) which iteratively solves subparts of the project. We present computational experiments on two datasets. The first benchmark set is used to illustrate the contribution of both the GA and the DBH. 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 instances of the resource-constrained project scheduling problem. We illustrate that GA is currently the best performing RCPSP meta-heuristic, and that the DBH further improves the performance of the GA

Suggested Citation

  • D. Debels & M. Vanhoucke, 2005. "A Decomposition-Based Heuristic For The Resource-Constrained Project Scheduling Problem," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/293, Ghent University, Faculty of Economics and Business Administration.
    • Handle: RePEc:rug:rugwps:05/293
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    File URL: http://wps-feb.ugent.be/Papers/wp_05_293.pdf
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    References listed on IDEAS

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    Cited by:

    1. Vanhoucke, Mario & Coelho, José, 2016. "An approach using SAT solvers for the RCPSP with logical constraints," European Journal of Operational Research, Elsevier, vol. 249(2), pages 577-591.
    2. M. Vanhoucke, 2007. "A genetic algorithm to investigate the trade-off between project lead time and net present value," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/456, Ghent University, Faculty of Economics and Business Administration.
    3. Horbach, Andrei, 2009. "A boolean satisfiability approach to the resource-constrained project scheduling problem," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 644, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    4. Zamani, Reza, 2013. "A competitive magnet-based genetic algorithm for solving the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 229(2), pages 552-559.
    5. Jiang, Yuanchun & Liu, Yezheng & Shang, Jennifer & Yildirim, Pinar & Zhang, Qingfu, 2018. "Optimizing online recurring promotions for dual-channel retailers: Segmented markets with multiple objectives," European Journal of Operational Research, Elsevier, vol. 267(2), pages 612-627.
    6. V. Van Peteghem & M. Vanhoucke, 2008. "A Genetic Algorithm for the Multi-Mode Resource-Constrained Project Scheduling Problem," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/494, Ghent University, Faculty of Economics and Business Administration.
    7. 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.
    8. Jürgen Kuster & Dietmar Jannach & Gerhard Friedrich, 2010. "Applying Local Rescheduling in response to schedule disruptions," Annals of Operations Research, Springer, vol. 180(1), pages 265-282, November.
    9. Edgar Gutiérrez Franco & Fernando La Torre Zurita & Gonzalo Mejía Delgadillo, 2007. "A genetic algorithm for the resource constrained project scheduling problem (RCPSP)," Investigación & Desarrollo, Universidad Privada Boliviana, vol. 1(1), pages 41-52.
    10. Coelho, José & Vanhoucke, Mario, 2011. "Multi-mode resource-constrained project scheduling using RCPSP and SAT solvers," European Journal of Operational Research, Elsevier, vol. 213(1), pages 73-82, August.
    11. Xichao Su & Wei Han & Yu Wu & Yong Zhang & Jie Liu, 2018. "A Proactive Robust Scheduling Method for Aircraft Carrier Flight Deck Operations with Stochastic Durations," Complexity, Hindawi, vol. 2018, pages 1-38, November.
    12. Gonzalo Muñoz & Daniel Espinoza & Marcos Goycoolea & Eduardo Moreno & Maurice Queyranne & Orlando Rivera Letelier, 2018. "A study of the Bienstock–Zuckerberg algorithm: applications in mining and resource constrained project scheduling," Computational Optimization and Applications, Springer, vol. 69(2), pages 501-534, March.
    13. Hongbo Li & Erik Demeulemeester, 2016. "A genetic algorithm for the robust resource leveling problem," Journal of Scheduling, Springer, vol. 19(1), pages 43-60, February.
    14. D. Debels & M. Vanhoucke, 2006. "Meta-heuristic resource-constrained project scheduling:solution space restrictions and neighbourhood extensions," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/387, Ghent University, Faculty of Economics and Business Administration.
    15. Lei Lei & Michael Pinedo & Lian Qi & Shengbin Wang & Jian Yang, 2015. "Personnel scheduling and supplies provisioning in emergency relief operations," Annals of Operations Research, Springer, vol. 235(1), pages 487-515, December.
    16. Peteghem, Vincent Van & Vanhoucke, Mario, 2010. "A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 201(2), pages 409-418, March.
    17. Nadia Chaudry & Ingunn Vermedal & Kjetil Fagerholt & Maria Fleischer Fauske & Magnus Stålhane, 2020. "A decomposition solution approach to the troops-to-tasks assignment in military peacekeeping operations," The Journal of Defense Modeling and Simulation, , vol. 17(4), pages 357-371, October.

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    Keywords

    project scheduling; genetic algorithms; decomposition;
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