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Approximation Algorithms and Hardness Results for the Joint Replenishment Problem with Constant Demands

  • Andreas S. Schulz
  • Claudio Telha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6942)

Abstract

In the Joint Replenishment Problem (JRP), the goal is to coordinate the replenishments of a collection of goods over time so that continuous demands are satisfied with minimum overall ordering and holding costs. We consider the case when demand rates are constant. Our main contribution is the first hardness result for any variant of JRP with constant demands. When replenishments per commodity are required to be periodic and the time horizon is infinite (which corresponds to the so-called general integer model with correction factor), we show that finding an optimal replenishment policy is at least as hard as integer factorization. This result provides the first theoretical evidence that the JRP with constant demands may have no polynomial-time algorithm and that relaxations and heuristics are called for. We then show that a simple modification of an algorithm by Wildeman et al. (1997) for the JRP gives a fully polynomial-time approximation scheme for the general integer model (without correction factor). We also extend their algorithm to the finite horizon case, achieving an approximation guarantee asymptotically equal to \(\sqrt{9/8}\).

Keywords

Hardness Result Approximation Guarantee General Integer Constant Demand Dynamic Policy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Andreas S. Schulz
    • 1
  • Claudio Telha
    • 2
  1. 1.Sloan School of Mgmt. & Operations Research CenterMITCambridgeUSA
  2. 2.Operations Research CenterMITCambridgeUSA

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