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A management system for decompositions in stochastic programming

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  • Robert Fourer
  • Leo Lopes

Abstract

This paper presents two contributions: A set of routines that manipulate instances of stochastic programming problems in order to make them more amenable for different solution approaches; and a development environment where these routines can be accessed and in which the modeler can examine aspects of the problem structure. The goal of the research is to reduce the amount of work, time, and cost involved in experimenting with different solution methods. Copyright Springer Science + Business Media, Inc. 2006

Suggested Citation

  • Robert Fourer & Leo Lopes, 2006. "A management system for decompositions in stochastic programming," Annals of Operations Research, Springer, vol. 142(1), pages 99-118, February.
    • Handle: RePEc:spr:annopr:v:142:y:2006:i:1:p:99-118:10.1007/s10479-006-6163-1
    DOI: 10.1007/s10479-006-6163-1
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    References listed on IDEAS

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    1. Pieter Klaassen, 1998. "Financial Asset-Pricing Theory and Stochastic Programming Models for Asset/Liability Management: A Synthesis," Management Science, INFORMS, vol. 44(1), pages 31-48, January.
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    6. R. T. Rockafellar & Roger J.-B. Wets, 1991. "Scenarios and Policy Aggregation in Optimization Under Uncertainty," Mathematics of Operations Research, INFORMS, vol. 16(1), pages 119-147, February.
    7. Matthias Nowak & Werner Römisch, 2000. "Stochastic Lagrangian Relaxation Applied to Power Scheduling in a Hydro-Thermal System under Uncertainty," Annals of Operations Research, Springer, vol. 100(1), pages 251-272, December.
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    Cited by:

    1. Robert Fourer & Leo Lopes, 2009. "StAMPL: A Filtration-Oriented Modeling Tool for Multistage Stochastic Recourse Problems," INFORMS Journal on Computing, INFORMS, vol. 21(2), pages 242-256, May.
    2. Joel Goh & Melvyn Sim, 2011. "Robust Optimization Made Easy with ROME," Operations Research, INFORMS, vol. 59(4), pages 973-985, August.

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