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A Differential Evolution Algorithm for Solving Resource Constrained Project Scheduling Problems

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Artificial Life and Computational Intelligence (ACALCI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9592))

Abstract

The resource constrained project scheduling problem is considered as a complex scheduling problem. In order to solve this NP-hard problem, an efficient differential evolution (DE) algorithm is proposed in this paper. In the algorithm, improved mutation and crossover operators are introduced with an aim to maintain feasibility for generated individuals and hence being able to converge quickly to the optimal solutions. The algorithm is tested on a set of well-known project scheduling problem library (PSPLIB), with instances of 30, 60, 90 and 120 activities. The proposed DE is shown to have superior performance in terms of lower average deviations from the optimal solutions compared to some of the state-of-the-art algorithms.

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Authors and Affiliations

  1. School of Engineering and Information Technology, University of New South Wales, Canberra, Australia

    Ismail M. Ali, Saber Mohammed Elsayed, Tapabrata Ray & Ruhul A. Sarker

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  1. Ismail M. Ali
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  2. Saber Mohammed Elsayed
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  3. Tapabrata Ray
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  4. Ruhul A. Sarker
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Corresponding author

Correspondence to Ismail M. Ali .

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Editors and Affiliations

  1. Canberra, Aust Capital Terr, Australia

    Tapabrata Ray

  2. University of New South Wales, Canberra, Aust Capital Terr, Australia

    Ruhul Sarker

  3. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

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Ali, I.M., Elsayed, S.M., Ray, T., Sarker, R.A. (2016). A Differential Evolution Algorithm for Solving Resource Constrained Project Scheduling Problems. In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_18

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  • DOI: https://doi.org/10.1007/978-3-319-28270-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28269-5

  • Online ISBN: 978-3-319-28270-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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