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Statistical decision analysis of stochastic linear programming problems

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  • Jerome Bracken
  • Richard M. Soland

Abstract

This paper presents a statistical decision analysis of a one‐stage linear programming problem with deterministic constraints and stochastic criterion function. Procedures for obtaining numerical results are given which are applicable to any problem having this general form. We begin by stating the statistical decision problems to be considered, and then discuss the expected value of perfect information and the expected value of sample information. In obtaining these quantities, use is made of the distribution of the optimal value of the linear programming problem with stochastic criterion function, and so we discuss Monte Carlo and numerical integration procedures for estimating the mean of this distribution. The case in which the random criterion vector has a multivariate Normal distribution is discussed separately, and more detailed methods are offered. We discuss dual problems, including some relationships of this work with other work in probabilistic linear programming. An example is given in Appendix A showing application of the methods to a sample problem. In Appendix B we consider the accuracy of a procedure for approximating the expected value of information.

Suggested Citation

  • Jerome Bracken & Richard M. Soland, 1966. "Statistical decision analysis of stochastic linear programming problems," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 13(3), pages 205-225, September.
    • Handle: RePEc:wly:navlog:v:13:y:1966:i:3:p:205-225
    DOI: 10.1002/nav.3800130302
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    Cited by:

    1. Sarper, Hüseyin, 1993. "Monte Carlo simulation for analysis of the optimum value distribution in stochastic mathematical programs," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 35(6), pages 469-480.

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