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Recent Advances in Computational Optimization pp 205–223Cite as

An Efficient Solution of the Resource Constrained Project Scheduling Problem Based on an Adaptation of the Developmental Genetic Programming

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 610))

Abstract

An adaptation of the Developmental Genetic Programming (DGP) for solving an extension of the Resource-Constrained Project Scheduling Problem (RCPSP) is investigated in the paper. In DGP genotypes (the search space) and phenotypes (the solution space) are distinguished and a genotype-to-phenotype mapping (GPM) is used. Thus, genotypes are evolved without any restrictions and the whole search space is explored. RCPSP is a well-known NP-hard problem but in its original formulation it does not take into consideration initial resource workload and it minimises the makespan. We consider a variant of the problem when resources are only partially available and a deadline is given but it is the cost of the project that should be minimized. The goal of the evolution is to find a procedure constructing the best solution of the problem for which the cost of the project is minimal. The paper presents new evolution process for the DGP as well as a comparison with other genetic approaches. Experimental results showed that our approach gives significantly better results compared with other methods.

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Notes

  1. 1.

    A genotype in classical GAs represents a solution of the problem, while in the DGP a genotype comprises a procedure for constructing that solution.

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Authors and Affiliations

  1. Department of Computer Science, Kielce University of Technology, Kielce, Poland

    Grzegorz Pawiński & Krzysztof Sapiecha

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  1. Grzegorz Pawiński
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  2. Krzysztof Sapiecha
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Correspondence to Grzegorz Pawiński .

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  1. Institute of Information and Communication Technology, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Stefka Fidanova

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Pawiński, G., Sapiecha, K. (2016). An Efficient Solution of the Resource Constrained Project Scheduling Problem Based on an Adaptation of the Developmental Genetic Programming. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 610. Springer, Cham. https://doi.org/10.1007/978-3-319-21133-6_12

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  • DOI: https://doi.org/10.1007/978-3-319-21133-6_12

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  • Online ISBN: 978-3-319-21133-6

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