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International Joint Conference on Computational Intelligence

IJCCI 2014: Computational Intelligence pp 49–62Cite as

Evolutionary Learning of Linear Composite Dispatching Rules for Scheduling

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 620))

Abstract

A prevalent approach to solving job shop scheduling problems is to combine several relatively simple dispatching rules such that they may benefit each other for a given problem space. Generally, this is done in an ad-hoc fashion, requiring expert knowledge from heuristics designers, or extensive exploration of suitable combinations of heuristics. The approach here is to automate that selection by translating dispatching rules into measurable features and optimising what their contribution should be via evolutionary search. The framework is straight forward and easy to implement and shows promising results. Various data distributions are investigated for both job shop and flow shop problems, as is scalability for higher dimensions. Moreover, the study shows that the choice of objective function for evolutionary search is worth investigating. Since the optimisation is based on minimising the expected mean of the fitness function over a large set of problem instances which can vary within the set, then normalising the objective function can stabilise the optimisation process away from local minima.

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Notes

  1. 1.

    Both code, written in C++, and problem instances used in their experiments can be found at: http://www.cs.colostate.edu/sched/generator/.

  2. 2.

    Optimum values are obtained by using a commercial software package [6].

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Author information

Authors and Affiliations

  1. Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Reykjavik, Iceland

    Helga Ingimundardottir & Thomas Philip Runarsson

Authors
  1. Helga Ingimundardottir
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  2. Thomas Philip Runarsson
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Corresponding author

Correspondence to Thomas Philip Runarsson .

Editor information

Editors and Affiliations

  1. Ingenería Informática, Escuela Técnica Superior de, Granada, Spain

    Juan Julian Merelo

  2. aSEEB-ISR-IST, Technical University of Lisbon (IST), Lisbon, Portugal

    Agostinho Rosa

  3. Facultad de Informática, University of Murcia, Murcia, Spain

    José M. Cadenas

  4. University of Coimbra, Coimbra, Portugal

    António Dourado

  5. Images, Signals and Intelligence, University PARIS-EST Créteil (UPEC), Créteil, France

    Kurosh Madani

  6. Instituto Politécnico de Setúbal (IPS), Setúbal, Portugal

    Joaquim Filipe

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© 2016 Springer International Publishing Switzerland

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Ingimundardottir, H., Runarsson, T.P. (2016). Evolutionary Learning of Linear Composite Dispatching Rules for Scheduling. In: Merelo, J.J., Rosa, A., Cadenas, J.M., Dourado, A., Madani, K., Filipe, J. (eds) Computational Intelligence. IJCCI 2014. Studies in Computational Intelligence, vol 620. Springer, Cham. https://doi.org/10.1007/978-3-319-26393-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-26393-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26391-5

  • Online ISBN: 978-3-319-26393-9

  • eBook Packages: EngineeringEngineering (R0)

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