Article ID: | iaor200968821 |
Country: | United States |
Volume: | 34 |
Issue: | 2 |
Start Page Number: | 397 |
End Page Number: | 416 |
Publication Date: | May 2009 |
Journal: | Mathematics of Operations Research |
Authors: | Huh Woonghee Tim, Janakiraman Ganesh, Rusmevichientong Paat, Muckstadt John A |
Keywords: | lost sales |
We consider a periodic-review, single-location, single-product inventory system with lost sales and positive replenishment lead times. It is well known that the optimal policy does not possess a simple structure. Motivated by recent results showing that base-stock policies perform well in these systems, we study the problem of finding the best base-stock policy in such a system. In contrast to the classical inventory literature, we assume that the manager does not know the demand distribution a priori but must make the replenishment decision in each period based only on the past sales (censored demand) data. We develop a nonparametric adaptive algorithm that generates a sequence of order-up-to levels whose running average of the inventory holding and lost sales penalty cost converges to the cost of the optimal base-stock policy, and we establish the cubic-root convergence rate of the algorithm. Our analysis is based on recent advances in stochastic online convex optimization and on the uniform ergodicity of Markov chains associated with bases-stock policies.