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Evolving Ensembles of Dispatching Rules Using Genetic Programming for Job Shop Scheduling

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Genetic Programming (EuroGP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9025))

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Abstract

Job shop scheduling (JSS) problems are important optimisation problems that have been studied extensively in the literature due to their applicability and computational difficulty. This paper considers static JSS problems with makespan minimisation, which are NP-complete for more than two machines. Because finding optimal solutions can be difficult for large problem instances, many heuristic approaches have been proposed in the literature. However, designing effective heuristics for different JSS problem domains is difficult. As a result, hyper-heuristics (HHs) have been proposed as an approach to automating the design of heuristics. The evolved heuristics have mainly been priority based dispatching rules (DRs). To improve the robustness of evolved heuristics generated by HHs, this paper proposes a new approach where an ensemble of rules are evolved using Genetic Programming (GP) and cooperative coevolution, denoted as Ensemble Genetic Programming for Job Shop Scheduling (EGP-JSS). The results show that EGP-JSS generally produces more robust rules than the single rule GP.

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Authors and Affiliations

  1. Evolutionary Computation Research Group, Victoria University of Wellington, PO Box 600, Wellington, 6140, New Zealand

    John Park, Su Nguyen, Mengjie Zhang & Mark Johnston

  2. International University - VNU HCMC, Ho Chi Minh City, Vietnam

    Su Nguyen

Authors
  1. John Park
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  2. Su Nguyen
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  3. Mengjie Zhang
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  4. Mark Johnston
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Corresponding authors

Correspondence to John Park or Mengjie Zhang .

Editor information

Editors and Affiliations

  1. Universidade de Coimbra, Coimbra, Portugal

    Penousal Machado

  2. Dalhousie University, Halifax, Nova Scotia, Canada

    Malcolm I. Heywood

  3. University College Dublin, Dublin, Ireland

    James McDermott

  4. Universidade Nova de Lisboa, Lisboa, Portugal

    Mauro Castelli

  5. Universidad de Granada, Granada, Spain

    Pablo García-Sánchez

  6. Aalborg University, Copenhagen, Denmark

    Paolo Burelli

  7. IT University of Copenhagen, Copenhagen, Denmark

    Sebastian Risi

  8. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

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Park, J., Nguyen, S., Zhang, M., Johnston, M. (2015). Evolving Ensembles of Dispatching Rules Using Genetic Programming for Job Shop Scheduling. In: Machado, P., et al. Genetic Programming. EuroGP 2015. Lecture Notes in Computer Science(), vol 9025. Springer, Cham. https://doi.org/10.1007/978-3-319-16501-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-16501-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16500-4

  • Online ISBN: 978-3-319-16501-1

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