Coercion through Optimization: A Classification of Optimization Techniques

Optimization techniques have been used to search for optimal values for decision variables and input variables associated with a simulation. More recently we have explored a mixe d-method approach, mixing optimization and code modification, for "coercing" simulations to meet new requirements. The coercion process, P, transforms a simulation that meets requirement R to meet a new requirement, R', without necessarily resorting to redesign or reimplementat ion. Coercion is a semi-automated process because it combines optimizations and code modifications. A coercion can be characterized as a regular expression P = ( m*, o* )* where m and o represent code modification and optimization respectively. P can consist of any number, including zero, of modifications and/or optimizations in any order. The use of optimization is encouraged because it increases the level of automation in the coercion process. We explore the question of determining best optimization techniques for coercion. We begin by considering classifications of optimization techniques already existing in the simulation community and apply these in the context of coercion. We consider issues such as computation time, set-up time, and avoidance of local minima that contribute towards the ease of use of each optimization technique. We discuss insights we expect to gain from the use of optimization and identify tools necessary to gain these insights. Additionally, we discuss how the result of one optimization may affect the outcome of another in a coercion sequence, P. We expect our results to serve as a guide to optimization method selection for simulation practitioners who wish to employ coercion.