An adaptive algorithm for finding the optimal base-stock policy in lost sales inventory systems with censored demand

An adaptive algorithm for finding the optimal base-stock policy in lost sales inventory systems with censored demand

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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: , , ,
Keywords: lost sales
Abstract:

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.

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