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

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  • D. DEBELS
  • M. VANHOUCKE

Abstract

The resource-constrained project scheduling problem (RCPSP) is one of the most challenging problems in project scheduling. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions for more challenging problem instances. In this paper, we present a new genetic algorithm (GA) that, in contrast of a conventional GA, makes use of two separate populations. This bi-population genetic algorithm (BPGA) operates on both a population of left-justified schedules and a population of right-justified schedules in order to fully exploit the features of the iterative forward/backward local search scheduling technique. Comparative computational results reveal that this procedure can be considered as today’s best performing RCPSP heuristic.

Suggested Citation

  • D. Debels & M. Vanhoucke, 2005. "A Bi-Population Based Genetic Algorithm for the Resource-Constrained Project Scheduling Problem," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/294, Ghent University, Faculty of Economics and Business Administration.
    • Handle: RePEc:rug:rugwps:05/294
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    1. Valls, Vicente & Quintanilla, Sacramento & Ballestin, Francisco, 2003. "Resource-constrained project scheduling: A critical activity reordering heuristic," European Journal of Operational Research, Elsevier, vol. 149(2), pages 282-301, September.
    2. Kolisch, Rainer, 1996. "Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation," European Journal of Operational Research, Elsevier, vol. 90(2), pages 320-333, April.
    3. Hartmann, Sonke & Kolisch, Rainer, 2000. "Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 127(2), pages 394-407, December.
    4. Hartmann, Sönke & Kolisch, R., 2000. "Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 11180, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. Li, K. Y. & Willis, R. J., 1992. "An iterative scheduling technique for resource-constrained project scheduling," European Journal of Operational Research, Elsevier, vol. 56(3), pages 370-379, February.
    6. J. Alcaraz & C. Maroto, 2001. "A Robust Genetic Algorithm for Resource Allocation in Project Scheduling," Annals of Operations Research, Springer, vol. 102(1), pages 83-109, February.
    7. Bouleimen, K. & Lecocq, H., 2003. "A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version," European Journal of Operational Research, Elsevier, vol. 149(2), pages 268-281, September.
    8. Kolisch, Rainer & Hartmann, Sönke, 1999. "Heuristic algorithms for the resource-constrained project scheduling problem: classification and computational analysis," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 10966, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    9. Pilar Tormos & Antonio Lova, 2001. "A Competitive Heuristic Solution Technique for Resource-Constrained Project Scheduling," Annals of Operations Research, Springer, vol. 102(1), pages 65-81, February.
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    Cited by:

    1. 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.
    2. 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.
    3. V. Van Peteghem & M. Vanhoucke, 2009. "An Artificial Immune System for the Multi-Mode Resource-Constrained Project Scheduling Problem," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/555, Ghent University, Faculty of Economics and Business Administration.
    4. 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.

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