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

Author(s)
Levi, Retsef; Shi, Cong
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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.
Date issued
2013-05
URI
http://hdl.handle.net/1721.1/87771
Department
Sloan School of Management
Journal
Operations Research
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Levi, Retsef, and Cong Shi. “Approximation Algorithms for the Stochastic Lot-Sizing Problem with Order Lead Times.” Operations Research 61, no. 3 (June 2013): 593–602.
Version: Author's final manuscript
ISSN
0030-364X
1526-5463

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