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Creating dispatching rules by simple ensemble combination

Author

Listed:
  • Marko Ɖurasević

    (University of Zagreb)

  • Domagoj Jakobović

    (University of Zagreb)

Abstract

Dispatching rules are often the method of choice for solving scheduling problems since they are fast, simple, and adaptive approaches. In recent years genetic programming has increasingly been used to automatically create dispatching rules for various scheduling problems. Since genetic programming is a stochastic approach, it needs to be executed several times to ascertain that good dispatching rules were obtained. This paper analyses whether combining several dispatching rules into an ensemble leads to performance improvements over the individual dispatching rules. Two methods for creating ensembles of dispatching rules, based on the sum and vote methods applied in machine learning, are used and their effectiveness is analysed with regards to the size of the ensemble, the genetic programming method used to generate the dispatching rules, the size of the evolved dispatching rules, and the method used for creating the ensembles. The results demonstrate that the generated ensembles achieve significant improvements over individual automatically generated dispatching rules.

Suggested Citation

  • Marko Ɖurasević & Domagoj Jakobović, 2019. "Creating dispatching rules by simple ensemble combination," Journal of Heuristics, Springer, vol. 25(6), pages 959-1013, December.
    • Handle: RePEc:spr:joheur:v:25:y:2019:i:6:d:10.1007_s10732-019-09416-x
    DOI: 10.1007/s10732-019-09416-x
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    References listed on IDEAS

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    1. Pickardt, Christoph W. & Hildebrandt, Torsten & Branke, Jürgen & Heger, Jens & Scholz-Reiter, Bernd, 2013. "Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems," International Journal of Production Economics, Elsevier, vol. 145(1), pages 67-77.
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    3. Allahverdi, Ali & Ng, C.T. & Cheng, T.C.E. & Kovalyov, Mikhail Y., 2008. "A survey of scheduling problems with setup times or costs," European Journal of Operational Research, Elsevier, vol. 187(3), pages 985-1032, June.
    4. Edmund K. Burke & Matthew Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & John R. Woodward, 2010. "A Classification of Hyper-heuristic Approaches," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 449-468, Springer.
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    Cited by:

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