Skip Navigation



IMA Journal of Management Mathematics Advance Access published online on August 22, 2007
IMA Journal of Management Mathematics, doi:10.1093/imaman/dpm027
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Di Domenica, N.
Right arrow Articles by Valente, P.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 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

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


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.