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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. Marshall L. Fisher, 1981. "The Lagrangian Relaxation Method for Solving Integer Programming Problems," Management Science, INFORMS, vol. 27(1), pages 1-18, January.
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    5. Marshall L. Fisher, 1985. "An Applications Oriented Guide to Lagrangian Relaxation," Interfaces, INFORMS, vol. 15(2), pages 10-21, April.
    6. John M. Mulvey & Hercules Vladimirou, 1992. "Stochastic Network Programming for Financial Planning Problems," Management Science, INFORMS, vol. 38(11), pages 1642-1664, November.
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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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