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Simulation optimization: a review of algorithms and applications

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Abstract

Simulation optimization refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation—discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise—various algorithms have been proposed in the literature. As one can imagine, there exist several competing algorithms for each of these classes of problems. This document emphasizes the difficulties in simulation optimization as compared to algebraic model-based mathematical programming makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the field.

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Authors and Affiliations

  1. Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA

    Satyajith Amaran & Nikolaos V. Sahinidis

  2. Engineering and Process Sciences, Core R&D, The Dow Chemical Company, 2301 N. Brazosport Blvd., Freeport, TX, 77541, USA

    Bikram Sharda

  3. Engineering and Process Sciences, Core R&D, The Dow Chemical Company, Research Campus 1776 Building, Midland, MI, 48667-1776, USA

    Scott J. Bury

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  1. Satyajith Amaran
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  2. Nikolaos V. Sahinidis
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Amaran, S., Sahinidis, N.V., Sharda, B. et al. Simulation optimization: a review of algorithms and applications. 4OR-Q J Oper Res 12, 301–333 (2014). https://doi.org/10.1007/s10288-014-0275-2

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