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Provably Near-Optimal Balancing Policies for Multi-Echelon Stochastic Inventory Control Models

Author

Listed:
  • Retsef Levi

    (Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142)

  • Robin Roundy

    (Department of Mathematics, Brigham Young University, Provo, Utah 84602)

  • Van Anh Truong

    (Industrial Engineering and Operations Research, Columbia University, New York, New York 10027)

  • Xinshang Wang

    (Industrial Engineering and Operations Research, Columbia University, New York, New York 10027)

Abstract

We develop the first algorithmic approach to compute provably good ordering policies for a multi-echelon, stochastic inventory system facing correlated, nonstationary and evolving demands over a finite horizon. Specifically, we study the serial system. Our approach is computationally efficient and provides worst-case guarantees. That is, the expected cost of the algorithms is guaranteed to be within a constant factor of the optimal expected cost; depending on the assumption the constant varies between two and three. Our algorithmic approach is based on an innovative scheme to account for costs in a multi-echelon, multi-period environment, as well as repeatedly balancing between opposing cost. The cost-accounting scheme, called a cause-effect cost-accounting scheme , is significantly different from traditional cost-accounting schemes in that it reallocates costs with the goal of assigning every unit of cost to the decision that caused the cost to be incurred. We believe it will have additional applications in other multi-echelon inventory models.

Suggested Citation

  • Retsef Levi & Robin Roundy & Van Anh Truong & Xinshang Wang, 2017. "Provably Near-Optimal Balancing Policies for Multi-Echelon Stochastic Inventory Control Models," Mathematics of Operations Research, INFORMS, vol. 42(1), pages 256-276, January.
    • Handle: RePEc:inm:ormoor:v:42:y:2017:i:1:p:256-276
    DOI: 10.1287/moor.2016.0805
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    References listed on IDEAS

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