Chapter 3Simulation Optimization: Improving Decisions under Uncertainty

Marco Better1, Fred Glover1, and Gary Kochenberger2

1 OptTek Systems, Inc., Boulder, CO, USA

2 Business Analytics, School of Business, University of Colorado-Denver, Denver, CO, USA

Introduction

Analytics has been defined as “the scientific process of transforming data into insight for making better decisions.” More and more organizations are using analytics to make better decisions and reduce risks. Analytics includes well-established methods such as mathematical optimization, simulation, probability theory, and statistics, as well as newer techniques that take elements from traditional methods and modify and/or combine them into robust frameworks in order to develop more powerful solution methods for many settings where traditional methods fall short. A prime example of the latter is the simulation–optimization framework. As its name implies, this method combines simulation and optimization in order to tackle complex situations where risk and uncertainty do not behave according to certain simplifying assumptions.

Taken separately, each method is critical, but limited in scope. On the one hand, optimization by itself provides an excellent method to select the best element in terms of some system performance criteria, from some set of available alternatives, in the absence of uncertainty. On the other hand, simulation is a tool that allows us to build a representation of a complex system in order to better understand ...

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