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A New Model for Stochastic Linear Programming

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  • William H. Evers

    (The University of Michigan)

Abstract

The linear programming formulation with random variation in the coefficient matrix is considered. A new model is proposed in which the random variation in the constraints is removed and terms dependent upon the distributions associated with the random constraints introduced into the objective function. The new objective function may be interpreted quite naturally as the sum of the original costs, the expected shortage (or overage) and the set-up cost. Comparisons made between the solutions of the linear program using the mean values of the distributions and the solution using the model show it is sometimes extremely costly to tacitly set the elements of the linear program at their mean values.

Suggested Citation

  • William H. Evers, 1967. "A New Model for Stochastic Linear Programming," Management Science, INFORMS, vol. 13(9), pages 680-693, May.
    • Handle: RePEc:inm:ormnsc:v:13:y:1967:i:9:p:680-693
    DOI: 10.1287/mnsc.13.9.680
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    Cited by:

    1. Lejeune, Miguel A. & Shen, Siqian, 2016. "Multi-objective probabilistically constrained programs with variable risk: Models for multi-portfolio financial optimization," European Journal of Operational Research, Elsevier, vol. 252(2), pages 522-539.
    2. Yiling Zhang & Jin Dong, 2022. "Building Load Control Using Distributionally Robust Chance-Constrained Programs with Right-Hand Side Uncertainty and the Risk-Adjustable Variants," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1531-1547, May.

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