Asymptotic optimality of Tailored Base-Surge policies in dual-sourcing inventory systems

TL;DR

This paper proves that Tailored Base-Surge policies are asymptotically optimal in dual-sourcing inventory systems as the regular source lead time increases, providing a theoretical foundation for their effectiveness.

Contribution

It offers the first rigorous proof of the asymptotic optimality of TBS policies in large lead time dual-sourcing inventory systems.

Findings

TBS policies are asymptotically optimal as regular lead time grows large.

The proof combines convexity, lower bounds, and Markov decision process analysis.

Extends the methodology of analyzing inventory models with large lead times.

Abstract

Dual-sourcing inventory systems, in which one supplier is faster (i.e. express) and more costly, while the other is slower (i.e. regular) and cheaper, arise naturally in many real-world supply chains. These systems are notoriously difficult to optimize due to the complex structure of the optimal solution and the curse of dimensionality, having resisted solution for over 40 years. Recently, so-called Tailored Base-Surge (TBS) policies have been proposed as a heuristic for the dual-sourcing problem. Under such a policy, a constant order is placed at the regular source in each period, while the order placed at the express source follows a simple order-up-to rule. Numerical experiments by several authors have suggested that such policies perform well as the lead time difference between the two sources grows large, which is exactly the setting in which the curse of dimensionality leads to the problem becoming intractable. However, providing a theoretical foundation for this phenomenon has remained a major open problem. In this paper, we provide such a theoretical foundation by proving that a simple TBS policy is indeed asymptotically optimal as the lead time of the regular source grows large, with the lead time of the express source held fixed. Our main proof technique combines novel convexity and lower-bounding arguments, an explicit implementation of the vanishing discount factor approach to analyzing infinite-horizon Markov decision processes, and ideas from the theory of random walks and queues, significantly extending the methodology and applicability of a novel framework for analyzing inventory models with large lead times recently introduced by Goldberg and co-authors in the context of lost-sales models with positive lead times.