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Efficient solution of many instances of a simulation-based optimization problem utilizing a partition of the decision space

Author

Listed:
  • Zuzana Nedělková

    (Chalmers University of Technology and University of Gothenburg)

  • Peter Lindroth

    (Volvo Group Trucks Technology)

  • Michael Patriksson

    (Chalmers University of Technology and University of Gothenburg)

  • Ann-Brith Strömberg

    (Chalmers University of Technology and University of Gothenburg)

Abstract

This paper concerns the solution of a class of mathematical optimization problems with simulation-based objective functions. The decision variables are partitioned into two groups, referred to as variables and parameters, respectively, such that the objective function value is influenced more by the variables than by the parameters. We aim to solve this optimization problem for a large number of parameter settings in a computationally efficient way. The algorithm developed uses surrogate models of the objective function for a selection of parameter settings, for each of which it computes an approximately optimal solution over the domain of the variables. Then, approximate optimal solutions for other parameter settings are computed through a weighting of the surrogate models without requiring additional expensive function evaluations. We have tested the algorithm’s performance on a set of global optimization problems differing with respect to both mathematical properties and numbers of variables and parameters. Our results show that it outperforms a standard and often applied approach based on a surrogate model of the objective function over the complete space of variables and parameters.

Suggested Citation

  • Zuzana Nedělková & Peter Lindroth & Michael Patriksson & Ann-Brith Strömberg, 2018. "Efficient solution of many instances of a simulation-based optimization problem utilizing a partition of the decision space," Annals of Operations Research, Springer, vol. 265(1), pages 93-118, June.
    • Handle: RePEc:spr:annopr:v:265:y:2018:i:1:d:10.1007_s10479-017-2721-y
    DOI: 10.1007/s10479-017-2721-y
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    References listed on IDEAS

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    1. Benoît Colson & Patrice Marcotte & Gilles Savard, 2007. "An overview of bilevel optimization," Annals of Operations Research, Springer, vol. 153(1), pages 235-256, September.
    2. L. Jeff Hong & Barry L. Nelson & Jie Xu, 2015. "Discrete Optimization via Simulation," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 9-44, Springer.
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    Cited by:

    1. Shivam Gupta & Sachin Modgil & Samadrita Bhattacharyya & Indranil Bose, 2022. "Artificial intelligence for decision support systems in the field of operations research: review and future scope of research," Annals of Operations Research, Springer, vol. 308(1), pages 215-274, January.
    2. Piotr Cheluszka & Amadeus Jagieła-Zając, 2022. "Identification of a Mathematical Model for the Transformation of Images for Stereo Correspondence Measurements of Mining Equipment," Energies, MDPI, vol. 15(17), pages 1-24, August.

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