Online Learning for Dual-Index Policies in Dual-Sourcing Systems

Jingwen Tang (), Boxiao Chen () and Cong Shi ()
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Jingwen Tang: Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Boxiao Chen: College of Business Administration, University of Illinois Chicago, Chicago, Illinois 60607
Cong Shi: Management Science, Miami Herbert Business School, University of Miami, Coral Gables, Florida 33146

Manufacturing & Service Operations Management, 2024, vol. 26, issue 2, 758-774

Abstract: Problem definition : We consider a periodic-review dual-sourcing inventory system with a regular source (lower unit cost but longer lead time) and an expedited source (shorter lead time but higher unit cost) under carried-over supply and backlogged demand. Unlike existing literature, we assume that the firm does not have access to the demand distribution a priori and relies solely on past demand realizations. Even with complete information on the demand distribution, it is well known in the literature that the optimal inventory replenishment policy is complex and state dependent. Therefore, we focus our attention on a class of popular, easy-to-implement, and near-optimal heuristic policies called the dual-index policy. Methodology/results : The performance measure is the regret, defined as the cost difference of any feasible learning algorithm against the full-information optimal dual-index policy. We develop a nonparametric online learning algorithm that admits a regret upper bound of O ( T log T ) , which matches the regret lower bound for any feasible learning algorithms up to a logarithmic factor. Our algorithm integrates stochastic bandits and sample average approximation techniques in an innovative way. As part of our regret analysis, we explicitly prove that the underlying Markov chain is ergodic and converges to its steady state exponentially fast via coupling arguments, which could be of independent interest. Managerial implications : Our work provides practitioners with an easy-to-implement, robust, and provably good online decision support system for managing a dual-sourcing inventory system.

Keywords: inventory; dual sourcing; dual-index policy; learning; bandits; sample average approximation (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:26:y:2024:i:2:p:758-774

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