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Prioritization via Stochastic Optimization

Author

Listed:
  • Ali Koç

    (Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • David P. Morton

    (Graduate Program in Operations Research and Industrial Engineering, The University of Texas at Austin, Austin, Texas 78712)

Abstract

We take a novel approach to decision problems involving binary activity-selection decisions competing for scarce resources. The literature approaches such problems by forming an optimal portfolio of activities. However, often practitioners instead form a rank-ordered list of activities and select those with the highest priority. We account for both viewpoints. We rank activities considering both the uncertainty in the problem parameters and the optimal portfolio that will be obtained once the uncertainty is revealed. We use stochastic integer programming as a modeling framework, and we apply our approach to a facility location problem and a multidimensional knapsack problem. We develop two sets of cutting planes to improve computation.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1865 . This paper was accepted by Dimitris Bertsimas, optimization.

Suggested Citation

  • Ali Koç & David P. Morton, 2015. "Prioritization via Stochastic Optimization," Management Science, INFORMS, vol. 61(3), pages 586-603, March.
    • Handle: RePEc:inm:ormnsc:v:61:y:2015:i:3:p:586-603
    DOI: 10.1287/mnsc.2013.1865
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    References listed on IDEAS

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    Cited by:

    1. Phebe Vayanos & Angelos Georghiou & Han Yu, 2026. "Robust Optimization with Decision-Dependent Information Discovery," Management Science, INFORMS, vol. 72(2), pages 1509-1528, February.

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