Scenario generation for stochastic programming and simulation: a modelling perspective

Stochastic programming (SP) brings together models of optimum resource allocation and models of randomness and thereby creates a robust decision-making framework. The models of randomness with their finite, discrete realizations are known as scenario generators. In this report, we consider alternative approaches to scenario generation in a generic form which can be used to formulate (a) two-stage (static) and (b) multi-stage dynamic SP models. We also investigate the modelling structure and software issues of integrating a scenario generator with an optimization model to construct SP recourse problems. We consider how the expected value and SP decision model results can be evaluated within a descriptive modelling framework of simulation. Illustrative examples and computational results are given in support of our investigation.