Modeling issues when using simulation to test the performance of mathematical programming models under stochastic conditions
Résumé
Discrete-event simulation (DES) models and discrete mathematical-programming optimization (DMPO) models are often used together in a variety of ways. This paper discusses the issues that modelers must address when using DES models to test the performance of DMPO models in a stochastic environment. The issues arise during validation of the simulation models – comparing the simulation results under deterministic conditions with results from deterministic optimization models. In our case, the issues are derived from validating simulation models that are used to test the performance of scheduling and resource allocation models (integer and mixed-integer programming optimization models) under various types of uncertainty. The models are from our work in crossdocking operations; however, we believe they are relevant to a wide variety of problem domains. In addition to describing the issues, we offer suggestions on how modelers might address the concerns.
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