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

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Author Info

  • Debels, Dieter
  • Vanhoucke, Mario

    ()
    (Vlerick Leuven Gent Management School)

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. Note

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Bibliographic Info

Paper provided by Vlerick Leuven Gent Management School in its series Vlerick Leuven Gent Management School Working Paper Series with number 2005-8.

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Length: 22 pages
Date of creation: 26 May 2005
Date of revision:
Handle: RePEc:vlg:vlgwps:2005-8

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Keywords: Networks; Topological structure; Graphs; Project Scheduling instances;

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References

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  1. 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).
  2. 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.
  3. 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).
  4. 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.
  5. 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.
  6. 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.
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Cited by:
  1. Edgar Gutiérrez Franco & Fernando La Torre Zurita & Gonzalo Mejía Delgadillo, 2007. "A genetic algorithm for the resource constrained project scheduling problem (RCPSP)," Investigación & Desarrollo 0307, Universidad Privada Boliviana, revised Mar 2007.
  2. 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.
  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.

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