Techniques for Monte Carlo optimizing

Simulation experiments are the primary analysis tools for designing complex systems. Simulation, however, must be linked with an optimization technique to be effectively used for systems design. This paper presents several optimization techniques involving both continuous and discrete controllable input parameters subject to a variety of constraints. In addition to these techniques of prescriptive analysis, we also discuss goal-seeking problems where a ‘good enough’ solution is preferred. Post-solution analysis tools such as stability and ‘what-if’ analysis are provided. Our goal is to provide a survey of the relevant literature, and set forth widely used techniques in a synthesized and unified manner to aid in a discriminating selection of the techniques most promising for a given simulation model.