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Resource Constrained Multi-project Scheduling: A Priority Rule Based Evolutionary Local Search Approach

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Intelligent and Evolutionary Systems

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 8))

Abstract

This paper considers a static resource constrained multi-project scheduling problem (RCMPSP) with two lateness objectives: project lateness and portfolio lateness. To solve the RCMPSP, we have proposed an evolutionary local search heuristic that uses a variable neighborhood (ELSH-VN) approach. The heuristic is further analyzed by incorporating different priority-rules. To judge the performance of these priority rule based heuristics, an extensive simulation-based analysis has been conducted with different scenario-based schedules. For the experimental study, we have considered a standard set of 77 generated RCMPSP test instances of 20 activities. The experimental analysis indicates that the proposed heuristic is able to solve multiple projects with reasonable computational burden. The influence of the variation of resource distribution and resource contention on the algorithm’s performance for different priority rules is also analyzed and discussed.

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Author information

Authors and Affiliations

  1. School of Engineering and Information Technology, University of New South Wales, Canberra, 2600, Australia

    Ripon K. Chakrabortty, Ruhul A. Sarker & Daryl L. Essam

Authors
  1. Ripon K. Chakrabortty
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  2. Ruhul A. Sarker
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  3. Daryl L. Essam
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Corresponding author

Correspondence to Ripon K. Chakrabortty .

Editor information

Editors and Affiliations

  1. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    George Leu

  2. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    Hemant Kumar Singh

  3. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    Saber Elsayed

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Chakrabortty, R.K., Sarker, R.A., Essam, D.L. (2017). Resource Constrained Multi-project Scheduling: A Priority Rule Based Evolutionary Local Search Approach. In: Leu, G., Singh, H., Elsayed, S. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-49049-6_6

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  • DOI: https://doi.org/10.1007/978-3-319-49049-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49048-9

  • Online ISBN: 978-3-319-49049-6

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