Hybridization of genetic algorithm and fully informed particle swarm for solving the multi-mode resource-constrained project scheduling problem

In this article, the genetic algorithm (GA) and fully informed particle swarm (FIPS) are hybridized for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with minimization of project makespan as the objective subject to resource and precedence constraints. In the propos...

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Vydáno v:Engineering optimization Ročník 49; číslo 3; s. 513 - 530
Hlavní autoři: Sebt, M. H., Afshar, M. R., Alipouri, Y.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 04.03.2017
Taylor & Francis Ltd
Témata:
Algorithms
combinatorial optimization
Decoding
Genetic algorithms
Genetics
hybrid GA-FIPS algorithm
multi-mode project scheduling
Optimization
Production scheduling
Project management
random key representation
resource constraints
Resource scheduling
Scheduling algorithms
Serials
Swarm intelligence
ISSN:0305-215X, 1029-0273
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Shrnutí:In this article, the genetic algorithm (GA) and fully informed particle swarm (FIPS) are hybridized for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with minimization of project makespan as the objective subject to resource and precedence constraints. In the proposed hybrid genetic algorithm-fully informed particle swarm algorithm (HGFA), FIPS is a popular variant of the particle swarm optimization algorithm. A random key and the related mode list representation schemes are used as encoding schemes, and the multi-mode serial schedule generation scheme (MSSGS) is considered as the decoding procedure. Furthermore, the existing mode improvement procedure in the literature is modified. The results show that the proposed mode improvement procedure remarkably improves the project makespan. Comparing the results of the proposed HGFA with other approaches using the well-known PSPLIB benchmark sets validates the effectiveness of the proposed algorithm to solve the MRCPSP.
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ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2016.1197610