IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v61y2015i3p586-603.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.2013.1865
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.2013.1865?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. 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.
    8. 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).
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eli Towle & James Luedtke, 2018. "New solution approaches for the maximum-reliability stochastic network interdiction problem," Computational Management Science, Springer, vol. 15(3), pages 455-477, October.
    2. Hans Kellerer & Vitaly A. Strusevich, 2016. "Optimizing the half-product and related quadratic Boolean functions: approximation and scheduling applications," Annals of Operations Research, Springer, vol. 240(1), pages 39-94, May.
    3. Arash Asadpour & Hamid Nazerzadeh, 2016. "Maximizing Stochastic Monotone Submodular Functions," Management Science, INFORMS, vol. 62(8), pages 2374-2391, August.
    4. Marek Adamczyk & Maxim Sviridenko & Justin Ward, 2016. "Submodular Stochastic Probing on Matroids," Mathematics of Operations Research, INFORMS, vol. 41(3), pages 1022-1038, August.
    5. Kübra Tanınmış & Markus Sinnl, 2022. "A Branch-and-Cut Algorithm for Submodular Interdiction Games," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2634-2657, September.
    6. Mohit Singh & Weijun Xie, 2020. "Approximation Algorithms for D -optimal Design," Mathematics of Operations Research, INFORMS, vol. 45(4), pages 1512-1534, November.
    7. Ortiz-Astorquiza, Camilo & Contreras, Ivan & Laporte, Gilbert, 2018. "Multi-level facility location problems," European Journal of Operational Research, Elsevier, vol. 267(3), pages 791-805.
    8. Dam, Tien Thanh & Ta, Thuy Anh & Mai, Tien, 2022. "Submodularity and local search approaches for maximum capture problems under generalized extreme value models," European Journal of Operational Research, Elsevier, vol. 300(3), pages 953-965.
    9. Beck, Yasmine & Ljubić, Ivana & Schmidt, Martin, 2023. "A survey on bilevel optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 311(2), pages 401-426.
    10. Majun Shi & Zishen Yang & Wei Wang, 2023. "Greedy Guarantees for Non-submodular Function Maximization Under Independent System Constraint with Applications," Journal of Optimization Theory and Applications, Springer, vol. 196(2), pages 516-543, February.
    11. Rad Niazadeh & Negin Golrezaei & Joshua Wang & Fransisca Susan & Ashwinkumar Badanidiyuru, 2023. "Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization," Management Science, INFORMS, vol. 69(7), pages 3797-3817, July.
    12. Mohammad Abouei Mehrizi & Federico Corò & Emilio Cruciani & Gianlorenzo D’Angelo, 2022. "Election control through social influence with voters’ uncertainty," Journal of Combinatorial Optimization, Springer, vol. 44(1), pages 635-669, August.
    13. Suning Gong & Qingqin Nong & Shuyu Bao & Qizhi Fang & Ding-Zhu Du, 2023. "A fast and deterministic algorithm for Knapsack-constrained monotone DR-submodular maximization over an integer lattice," Journal of Global Optimization, Springer, vol. 85(1), pages 15-38, January.
    14. Emily M. Craparo & Mumtaz Karatas & Tobias U. Kuhn, 2017. "Sensor placement in active multistatic sonar networks," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(4), pages 287-304, June.
    15. Alexandre D. Jesus & Luís Paquete & Arnaud Liefooghe, 2021. "A model of anytime algorithm performance for bi-objective optimization," Journal of Global Optimization, Springer, vol. 79(2), pages 329-350, February.
    16. Oded Berman & Dmitry Krass & Mozart B. C. Menezes, 2007. "Facility Reliability Issues in Network p -Median Problems: Strategic Centralization and Co-Location Effects," Operations Research, INFORMS, vol. 55(2), pages 332-350, April.
    17. Hongjie Guo & Jianzhong Li & Hong Gao, 2022. "Data source selection for approximate query," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2443-2459, November.
    18. Bin Liu & Miaomiao Hu, 2022. "Fast algorithms for maximizing monotone nonsubmodular functions," Journal of Combinatorial Optimization, Springer, vol. 43(5), pages 1655-1670, July.
    19. repec:dgr:rugsom:99a17 is not listed on IDEAS
    20. Klages-Mundt, Ariah & Minca, Andreea, 2022. "Optimal intervention in economic networks using influence maximization methods," European Journal of Operational Research, Elsevier, vol. 300(3), pages 1136-1148.
    21. Xin Chen & Qingqin Nong & Yan Feng & Yongchang Cao & Suning Gong & Qizhi Fang & Ker-I Ko, 2017. "Centralized and decentralized rumor blocking problems," Journal of Combinatorial Optimization, Springer, vol. 34(1), pages 314-329, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:61:y:2015:i:3:p:586-603. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.