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IMA Journal of Management Mathematics Advance Access originally published online on August 22, 2007
IMA Journal of Management Mathematics 2009 20(1):1-38; doi:10.1093/imaman/dpm027
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© The authors 2007. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

Scenario generation for stochastic programming and simulation: a modelling perspective

Nico Di Domenica, Cormac Lucas{dagger}, Gautam Mitra and Patrick Valente

Department of Mathematical Sciences, CARISMA, Brunel University, Uxbridge, Middlesex UB8 3PH, UK

{dagger} Email: cormac.a.lucas{at}brunel.ac.uk

Received on 7 April 2007. Accepted on 3 May 2007.

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.

Keywords: scenario generation; sampling; simulation; stochastic programming environment


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