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Polymatroid Optimization, Submodularity, and Joint Replenishment Games

Author

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  • Simai He

    (Department of Management Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong)

  • Jiawei Zhang

    (Department of Information, Operations, and Management Sciences, Stern School of Business, New York University, New York, New York 10012)

  • Shuzhong Zhang

    (Industrial and Systems Engineering Program, University of Minnesota, Minneapolis, Minnesota 55455)

Abstract

In this paper we consider the problem of maximizing a separable concave function over a polymatroid. More specifically, we study the submodularity of its optimal objective value in the parameters of the objective function. This question is interesting in its own right and is encountered in many applications. But our research has been motivated mainly by a cooperative game associated with the well-known joint replenishment model. By applying our general results on polymatroid optimization, we prove that this cooperative game is submodular (i.e., its characteristic cost function is submodular) if the joint setup cost is a normalized and nondecreasing submodular function. Furthermore, the same result holds true for a more general one-warehouse multiple retailer game, which affirmatively answers an open question posed by Anily and Haviv [Anily, S., M. Haviv. 2007. The cost allocation problem for the first order interaction joint replenishment model. Oper. Res. 55 (2) 292--302].

Suggested Citation

  • Simai He & Jiawei Zhang & Shuzhong Zhang, 2012. "Polymatroid Optimization, Submodularity, and Joint Replenishment Games," Operations Research, INFORMS, vol. 60(1), pages 128-137, February.
    • Handle: RePEc:inm:oropre:v:60:y:2012:i:1:p:128-137
    DOI: 10.1287/opre.1110.1000
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    References listed on IDEAS

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    Cited by:

    1. Jingjing Tan & Yicheng Xu & Dongmei Zhang & Xiaoqing Zhang, 2023. "On streaming algorithms for maximizing a supermodular function plus a MDR-submodular function on the integer lattice," Journal of Combinatorial Optimization, Springer, vol. 45(2), pages 1-19, March.
    2. Simai He & Jay Sethuraman & Xuan Wang & Jiawei Zhang, 2017. "A NonCooperative Approach to Cost Allocation in Joint Replenishment," Operations Research, INFORMS, vol. 65(6), pages 1562-1573, December.
    3. Luo, Chunlin & Zhou, Xiaoyang & Lev, Benjamin, 2022. "Core, shapley value, nucleolus and nash bargaining solution: A Survey of recent developments and applications in operations management," Omega, Elsevier, vol. 110(C).
    4. Martijn H. H. Schoot Uiterkamp & Marco E. T. Gerards & Johann L. Hurink, 2022. "On a Reduction for a Class of Resource Allocation Problems," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1387-1402, May.
    5. Lindong Liu & Xiangtong Qi & Zhou Xu, 2016. "Computing Near-Optimal Stable Cost Allocations for Cooperative Games by Lagrangian Relaxation," INFORMS Journal on Computing, INFORMS, vol. 28(4), pages 687-702, November.
    6. Nguyen, Christine & Dessouky, Maged & Toriello, Alejandro, 2014. "Consolidation strategies for the delivery of perishable products," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 69(C), pages 108-121.

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