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Monte Carlo simulation for analysis of the optimum value distribution in stochastic mathematical programs

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  • Sarper, Hüseyin

Abstract

This paper shows how simulation can be used to quickly solve an otherwise complex mathematical problem of derivation of the optimum value distribution function in stochastic mathematical programs. Two software packages (LINDO and UNIFIT II) are used along with a FORTRAN code in simulating and analyzing a given linear program with stochastic coefficients. Two previously analytically solved problems are resolved using simulation to show that simulation is indeed an effective method of solution.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:matcom:v:35:y:1993:i:6:p:469-480
    DOI: 10.1016/0378-4754(93)90065-3
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    References listed on IDEAS

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    1. 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.
    2. Hiroyuki Itami, 1974. "Expected Objective Value of a Stochastic Linear Program and the Degree of Uncertainty of Parameters," Management Science, INFORMS, vol. 21(3), pages 291-301, November.
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    Cited by:

    1. Qin, Rui & Liu, Yan-Kui, 2010. "Modeling data envelopment analysis by chance method in hybrid uncertain environments," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(5), pages 922-950.

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