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Computation in Discrete Stochastic Programs with Recourse

Author

Listed:
  • Stanley J. Garstka

    (University of Chicago, Chicago, Illinois)

  • David P. Rutenberg

    (Carnegie-Mellon University, Pittsburgh, Pennsylvania)

Abstract

This paper presents a solution procedure for discrete stochastic programs with recourse (linear programs under uncertainty). It views the m stochastic elements of the requirements vector as an m -dimensional space in which each combination of the discrete values is a lattice point. For a given second-stage basis, certain of the lattice points are feasible. A procedure is presented to delete infeasible points from the space. Thus, the aggregate probability associated with points feasible for this basis can be enumerated, and used to weight the vector of dual variables defined by the basis. Finally, the paper presents a systematic procedure for changing optimal bases so that a feasible and optimal basis is found for every lattice point.

Suggested Citation

  • Stanley J. Garstka & David P. Rutenberg, 1973. "Computation in Discrete Stochastic Programs with Recourse," Operations Research, INFORMS, vol. 21(1), pages 112-122, February.
    • Handle: RePEc:inm:oropre:v:21:y:1973:i:1:p:112-122
    DOI: 10.1287/opre.21.1.112
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    Cited by:

    1. Ilbin Lee & Stewart Curry & Nicoleta Serban, 2019. "Solving Large Batches of Linear Programs," INFORMS Journal on Computing, INFORMS, vol. 31(2), pages 302-317, April.
    2. Nonås, Sigrid Lise, 2009. "Finding and identifying optimal inventory levels for systems with common components," European Journal of Operational Research, Elsevier, vol. 193(1), pages 98-119, February.

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