Approximation Algorithms for the Stochastic Lot-Sizing Problem with Order Lead Times
Author(s)Levi, Retsef; Shi, Cong
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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.
DepartmentSloan School of Management
Institute for Operations Research and the Management Sciences (INFORMS)
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.
Author's final manuscript