A Nonparametric Learning Algorithm for a Stochastic Multi-echelon Inventory Problem

• 成果类型: Article
• 署名作者: Yang, Cong; Huh, Woonghee Tim
• 署名单位: University of British Columbia
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1177/10591478241231858
• 发表日期: 2024
• 页码: 701-720
• 关键词: Multi-echelon serial system; INVENTORY CONTROL; online learning and optimization; nonparametric learning algorithm; Regret Analysis

摘要

We consider a periodic-review single-product multi-echelon inventory problem with instantaneous replenishment. In each period, the decision-maker makes ordering decisions for all echelons. Any unsatisfied demand is back-ordered, and any excess inventory is carried to the next period. In contrast to the classic inventory literature, we assume that the information of the demand distribution is not known a priori, and the decision-maker observes demand realizations over the planning horizon. We propose a nonparametric algorithm that generates a sequence of adaptive ordering decisions based on the stochastic gradient descent method. We compare the T -period cost of our algorithm to the clairvoyant, who knows the underlying demand distribution in advance, and we prove that the expected T -period regret is at most O ( T ) , matching a lower bound for this problem.