Discrete Optimization via Simulation

Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 216)

Abstract

This chapter describes tools and techniques that are useful for optimization via simulation—maximizing or minimizing the expected value of a performance measure of a stochastic simulation—when the decision variables are discrete. Ranking and selection, globally and locally convergent random search and ordinal optimization are covered, along with a collection of “enhancements” that may be applied to many different discrete optimization via simulation algorithms. We also provide strategies for using commercial solvers.

Notes

Acknowledgements

This work was supported in part by the National Science Foundation 1099 under Grant CMMI-1233376, and by the Hong Kong Research Grants Council under Project 613011, 613012 and N_HKUST626/10.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.City University of Hong KongKowloon TongHong Kong
  2. 2.Northwestern UniversityEvanstonUSA
  3. 3.George Mason UniversityFairfaxUSA

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