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A self‐adapting genetic algorithm for project scheduling under resource constraints

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  • Sönke Hartmann

Abstract

This papers deals with the classical resource‐constrained project scheduling problem (RCPSP). There, the activities of a project have to be scheduled subject to precedence and resource constraints. The objective is to minimize the makespan of the project. We propose a new heuristic called self‐adapting genetic algorithm to solve the RCPSP. The heuristic employs the well‐known activity list representation and considers two different decoding procedures. An additional gene in the representation determines which of the two decoding procedures is actually used to compute a schedule for an individual. This allows the genetic algorithm to adapt itself to the problem instance actually solved. That is, the genetic algorithm learns which of the alternative decoding procedures is the more successful one for this instance. In other words, not only the solution for the problem, but also the algorithm itself is subject to genetic optimization. Computational experiments show that the mechanism of self‐adaptation is capable to exploit the benefits of both decoding procedures. Moreover, the tests show that the proposed heuristic is among the best ones currently available for the RCPSP. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 433–448, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10029

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  • Sönke Hartmann, 2002. "A self‐adapting genetic algorithm for project scheduling under resource constraints," Naval Research Logistics (NRL), John Wiley & Sons, vol. 49(5), pages 433-448, August.
    • Handle: RePEc:wly:navres:v:49:y:2002:i:5:p:433-448
    DOI: 10.1002/nav.10029
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    References listed on IDEAS

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    Cited by:

    1. Bernardo F. Almeida & Isabel Correia & Francisco Saldanha-da-Gama, 2018. "A biased random-key genetic algorithm for the project scheduling problem with flexible resources," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 283-308, July.
    2. 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.
    3. Luise-Sophie Hoffmann & Carolin Kellenbrink & Stefan Helber, 2020. "Simultaneous structuring and scheduling of multiple projects with flexible project structures," Journal of Business Economics, Springer, vol. 90(5), pages 679-711, June.
    4. Abdollah Arasteh, 2020. "Considering Project Management Activities for Engineering Design Groups," SN Operations Research Forum, Springer, vol. 1(4), pages 1-29, December.

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