Approximation Algorithms for the Stochastic Lot-Sizing Problem with Order Lead Times

Retsef Levi () and Cong Shi ()
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Retsef Levi: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Cong Shi: Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109

Operations Research, 2013, vol. 61, issue 3, 593-602

Abstract: We develop new algorithmic approaches to compute provably near-optimal policies for multiperiod stochastic lot-sizing inventory models with positive lead times, general demand distributions, and dynamic forecast updates. The policies that are developed have worst-case performance guarantees of 3 and typically perform very close to optimal in extensive computational experiments. The newly proposed algorithms employ a novel randomized decision rule. We believe that these new algorithmic and performance analysis techniques could be used in designing provably near-optimal randomized algorithms for other stochastic inventory control models and more generally in other multistage stochastic control problems.

Keywords: inventory; stochastic lot-sizing; approximation algorithms; randomized cost balancing (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (17)

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