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Solving a Class of Stochastic Minimization Problems

Author

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  • Michael P. Bailey

    (Naval Postgraduate School, Monterey, California)

Abstract

This work gives a methodology for analyzing a class of discrete minimization problems with random element weights. The minimum weight solution is shown to be an absorbing state in a Markov chain, while the distribution of weight of the minimum weight element is shown to be of phase type. We then present two-sided bounds for matroids with NBUE distributed weights, as well as for weights with bounded positive hazard rates. We illustrate our method using a realistic military communications problem.

Suggested Citation

  • Michael P. Bailey, 1994. "Solving a Class of Stochastic Minimization Problems," Operations Research, INFORMS, vol. 42(3), pages 428-438, June.
    • Handle: RePEc:inm:oropre:v:42:y:1994:i:3:p:428-438
    DOI: 10.1287/opre.42.3.428
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