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Identifying efficient solutions via simulation: myopic multi-objective budget allocation for the bi-objective case

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  • Juergen Branke

    (Warwick Business School, University of Warwick)

  • Wen Zhang

    (Warwick Business School, University of Warwick)

Abstract

Simulation optimisation offers great opportunities in the design and optimisation of complex systems. In the presence of multiple objectives, there is usually no single solution that performs best on all objectives. Instead, there are several Pareto-optimal (efficient) solutions with different trade-offs which cannot be improved in any objective without sacrificing performance in another objective. For the case where alternatives are evaluated on multiple stochastic criteria, and the performance of an alternative can only be estimated via simulation, we consider the problem of efficiently identifying the Pareto-optimal designs out of a (small) given set of alternatives. We present a simple myopic budget allocation algorithm for multi-objective problems and propose several variants for different settings. In particular, this myopic method only allocates one simulation sample to one alternative in each iteration. This paper shows how the algorithm works in bi-objective problems under different settings. Empirical tests show that our algorithm can significantly reduce the necessary simulation budget.

Suggested Citation

  • Juergen Branke & Wen Zhang, 2019. "Identifying efficient solutions via simulation: myopic multi-objective budget allocation for the bi-objective case," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(3), pages 831-865, September.
    • Handle: RePEc:spr:orspec:v:41:y:2019:i:3:d:10.1007_s00291-019-00561-0
    DOI: 10.1007/s00291-019-00561-0
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    References listed on IDEAS

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