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SPEPS Preprint

Author(s): Berkin Toktas, University of Washington, Industrial Engineering
Joyce W. Yen, University of Washington, ADVANCE Center for Institutional Change
Zelda B. Zabinsky, University of Washington, Industrial Engineering
Title: A stochastic programming approach to resource-constrained assignment problems
Date of Acceptance: 17.05.2004
Submission Date: 17.02.2004
Series Title: Stochastic Programming E-Print Series
(SPEPS)
Editors: Julie L. Higle; Werner Römisch; Surrajeet Sen
Complete Preprint: pdf (urn:nbn:de:kobv:11-10059526)
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Abstract (eng):
We address the resource-constrained generalizations of the assignment problem with uncertain resource capacities, where the resource capacities have an unknown distribution that can be sampled. We propose three stochastic programming-based formulations that can be used to solve this problem, and provide exact and approximate solution techniques for the resulting models. We also present numerical results for a large set of numerical problems. The results indicate that the solutions obtained using the stochastic programming approaches perform significantly better than those obtained using expected values of capacities in a deterministic solution strategy. In addition, stochastic-programming-based approximations are computationally as efficient as deterministic techniques.
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