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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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    1. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions - 1," LIDAM Reprints CORE 334, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Hochbaum, Dorit S., 2009. "Dynamic evolution of economically preferred facilities," European Journal of Operational Research, Elsevier, vol. 193(3), pages 649-659, March.
    3. Guglielmo Lulli & Suvrajeet Sen, 2004. "A Branch-and-Price Algorithm for Multistage Stochastic Integer Programming with Application to Stochastic Batch-Sizing Problems," Management Science, INFORMS, vol. 50(6), pages 786-796, June.
    4. Gerald Brown & Matthew Carlyle & Javier Salmerón & Kevin Wood, 2006. "Defending Critical Infrastructure," Interfaces, INFORMS, vol. 36(6), pages 530-544, December.
    5. J. M. W. Rhys, 1970. "A Selection Problem of Shared Fixed Costs and Network Flows," Management Science, INFORMS, vol. 17(3), pages 200-207, November.
    6. Brian C. Dean & Michel X. Goemans & Jan Vondrák, 2008. "Approximating the Stochastic Knapsack Problem: The Benefit of Adaptivity," Mathematics of Operations Research, INFORMS, vol. 33(4), pages 945-964, November.
    7. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions," LIDAM Reprints CORE 341, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Onur Şeref & Ravindra K. Ahuja & James B. Orlin, 2009. "Incremental Network Optimization: Theory and Algorithms," Operations Research, INFORMS, vol. 57(3), pages 586-594, June.
    9. C. N. Potts, 1985. "A Lagrangean Based Branch and Bound Algorithm for Single Machine Sequencing with Precedence Constraints to Minimize Total Weighted Completion Time," Management Science, INFORMS, vol. 31(10), pages 1300-1311, October.
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