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Solving a Class of Stochastic Minimization Problems

Michael P. Bailey
Operations Research
Vol. 42, No. 3 (May - Jun., 1994), pp. 428-438
Published by: INFORMS
Stable URL: http://www.jstor.org/stable/171883
Page Count: 11
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Solving a Class of Stochastic Minimization Problems
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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.

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