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A biased random-key genetic algorithm for the project scheduling problem with flexible resources

Author

Listed:
  • Bernardo F. Almeida

    (Universidade de Lisboa)

  • Isabel Correia

    (Universidade Nova de Lisboa)

  • Francisco Saldanha-da-Gama

    (Universidade de Lisboa)

Abstract

In this paper, we investigate a resource-constrained project scheduling problem with flexible resources. This is an $$\mathcal {NP}$$ NP -hard combinatorial optimization problem that consists of scheduling a set of activities requiring specific resource units of several skills. The goal is to minimize the makespan of the project. We propose a biased random-key genetic algorithm for computing feasible solutions for the referred problem. We study different decoding mechanisms: an already existing method in the literature, a new adapted serial scheduling generation scheme, and a combination of both. The new procedure is tested using a set of benchmark instances of the problem. The results provide strong evidence that the new heuristic is robust and yields high-quality feasible solutions.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:topjnl:v:26:y:2018:i:2:d:10.1007_s11750-018-0472-9
    DOI: 10.1007/s11750-018-0472-9
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    References listed on IDEAS

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    Cited by:

    1. Hartmann, Sönke & Briskorn, Dirk, 2022. "An updated survey of variants and extensions of the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 297(1), pages 1-14.
    2. Meya Haroune & Cheikh Dhib & Emmanuel Neron & Ameur Soukhal & Hafed Mohamed Babou & Mohamedade Farouk Nanne, 2023. "Multi-project scheduling problem under shared multi-skill resource constraints," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(1), pages 194-235, April.

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