Approximation Algorithms for the Stochastic Lot-Sizing Problem with Order Lead Times
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Levi_Approximation algorithms.pdf
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Author(s)
Levi, Retsef
Shi, Cong
Date Issued
May 2013
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
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.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1287/opre.2013.1162
