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- Title
Single-Product Assemble-to-Order Systems with Exogenous Lead Times.
- Authors
Muharremoglu, Alp; Yang, Nan; Geng, Xin
- Abstract
Efficient Algorithm for Base Stock Policy Optimization in Single-Product Assemble-to-Order Systems with Stochastic Lead Times Single-product assemble-to-order systems can be commonly observed in industrial settings. For example, firms targeting a niche market often choose to offer a single product; moreover, sole-product rollout is usually a preferable business strategy for manufacturers promoting a brand-new product or trying to penetrate a new market. However, maintaining an effective inventory management for those systems is a notoriously difficult problem, especially when the order lead times exhibit stochastic patterns. In "Single-Product Assemble-to-Order Systems with Exogenous Lead Times," Muharremoglu, Yang, and Geng investigate the case with a special type of stochastic lead time and utilize a certain analysis technique to develop an efficient algorithm for the performance evaluation of base stock policies. In addition, efficiently computable upper and lower bounds are proved based on the key idea of the algorithm. Extensive numerical studies show that the heuristics and the approximation methods developed from those bounds perform well in base stock optimization. We study a single-product assemble-to-order (ATO) system with exogenous lead times operated under a component base stock policy. Both demand and component lead times are random and are assumed to have finite supports. The challenge of evaluating a base stock policy in an ATO system with random lead times lies in the fact that one needs to compute the distribution of the minimum of n correlated random variables, where n is the number of components. The correlation arises because the replenishment quantities of different components are all contingent on the demand for the final product. We tackle this problem by first looking into two special cases, namely, the one with independent and identically distributed (i.i.d.) lead times and the one with sequential lead times. We give two algorithms for the i.i.d. lead time case, but both of them have exponential complexity in some system parameter. Then, we investigate the case of sequential lead times and utilize its particular structure to develop an algorithm with polynomial complexity. This is the first efficient algorithm for the performance evaluation of base stock policies in an assemble-to-order system with random lead times. Furthermore, using the method as an evaluation oracle in a steepest descent algorithm, we also obtain a polynomial time algorithm to optimize base stock for the case of sequential lead times. For the general case of exogenous lead times, we provide efficiently computable upper and lower bounds, which are identified based on the idea of comparing the level of correlation among component orders. Via extensive numerical studies, we test the performance of approximation methods developed from the identified bounds. We find that our proposed methods have advantages over other approximation methods in the literature, and both of them perform well as part of an approximated base stock optimization algorithm. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2009.0365.
- Subjects
LEAD time (Supply chain management); POLYNOMIAL time algorithms; OPTIMIZATION algorithms; INVENTORY management systems; STOCHASTIC systems
- Publication
Operations Research, 2024, Vol 72, Issue 3, p916
- ISSN
0030-364X
- Publication type
Article
- DOI
10.1287/opre.2009.0365