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An Artificial Immune System for the Multi-Mode Resource-Constrained Project Scheduling Problem

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  • V. VAN PETEGHEM

    ()

  • M. VANHOUCKE

    ()

Abstract

In this paper, an Artificial Immune System (AIS) for the multi-mode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project, is presented. The AIS algorithm makes use of mechanisms which are inspired on the vertebrate immune system performed on an initial population set. This population set is generated with a controlled search method, based on experimental results which revealed a link between predefined profit values of a mode assignment and its makespan. The impact of the algorithmic parameters and the initial population generation method is observed and detailed comparative computational results for the MRCPSP are presented.

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Bibliographic Info

Paper provided by Ghent University, Faculty of Economics and Business Administration in its series Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium with number 09/555.

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Length: 13 pages
Date of creation: Jan 2009
Date of revision:
Handle: RePEc:rug:rugwps:09/555

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  1. 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.
  2. F. Brian Talbot, 1982. "Resource-Constrained Project Scheduling with Time-Resource Tradeoffs: The Nonpreemptive Case," Management Science, INFORMS, vol. 28(10), pages 1197-1210, October.
  3. Rainer Kolisch & Arno Sprecher & Andreas Drexl, 1995. "Characterization and Generation of a General Class of Resource-Constrained Project Scheduling Problems," Management Science, INFORMS, vol. 41(10), pages 1693-1703, October.
  4. Mori, Masao & Tseng, Ching Chih, 1997. "A genetic algorithm for multi-mode resource constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 100(1), pages 134-141, July.
  5. Debels, Dieter & Vanhoucke, Mario, 2005. "A Bi-Population Based Genetic Algorithm for the Resource-Constrained Project Scheduling Problem," Vlerick Leuven Gent Management School Working Paper Series 2005-8, Vlerick Leuven Gent Management School.
  6. Ozdamar, Linet & Ulusoy, Gunduz, 1994. "A local constraint based analysis approach to project scheduling under general resource constraints," European Journal of Operational Research, Elsevier, vol. 79(2), pages 287-298, December.
  7. Slowinski, Roman & Soniewicki, Boleslaw & Weglarz, Jan, 1994. "DSS for multiobjective project scheduling," European Journal of Operational Research, Elsevier, vol. 79(2), pages 220-229, December.
  8. Vanhoucke, Mario & Coelho, Jose & Debels, Dieter & Maenhout, Broos & Tavares, Luis V., 2008. "An evaluation of the adequacy of project network generators with systematically sampled networks," European Journal of Operational Research, Elsevier, vol. 187(2), pages 511-524, June.
  9. Kolisch, Rainer & Hartmann, Sonke, 2006. "Experimental investigation of heuristics for resource-constrained project scheduling: An update," European Journal of Operational Research, Elsevier, vol. 174(1), pages 23-37, October.
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Cited by:
  1. 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.

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