An Adaptive Algorithm for Finding the Optimal Base-Stock Policy in Lost Sales Inventory Systems with Censored Demand
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, and she must make the replenishment decision in each period based on the past sales (censored demand) data. We develop a nonparametric adaptive algorithm that generates a sequence of order-up-to levels whose T-period running average of the inventory holding and lost sales penalty cost converges to the cost of the optimal base-stock policy at the rate of O(1/T 1/3 ). Our analysis is based on the uniform ergodicity to establish the convergence rate of the inventory process under a base-stock policy, and also on recent advances in stochastic online convex optimization to prove the performance of the proposed algorithm.
国际会议
2007 International Conference on Manufacturing & Service Operations Management(2007制造与服务运作管理国际学术会议)
北京
英文
2007-06-18（万方平台首次上网日期，不代表论文的发表时间）