The optimizing-simulator

Abstract

There have been two primary modeling and algorithmic strategies for modeling operational problems in transportation and logistics: simulation, offering tremendous modeling flexibility, and optimization, which offers the intelligence of math programming. Each offers significant theoretical and practical advantages. In this article, we show that you can model complex problems using a range of decision functions, including both rule-based and cost-based logic, and spanning different classes of information. We show how different types of decision functions can be designed using up to four classes of information. The choice of which information classes to use is a modeling choice, and requires making specific choices in the representation of the problem. We illustrate these ideas in the context of modeling military airlift, where simulation and optimization have been viewed as competing methodologies. Our goal is to show that these are simply different flavors of a series of integrated modeling strategies.