Dynamic Capacity Allocation to Customers Who Remember Past Service
研究供应商如何动态分配有限产能给一群记住过去服务水平的客户，发现考虑客户记忆效应的策略能显著提升利润，并开发了一种近似动态规划方法。
We study the problem faced by a supplier deciding how to dynamically allocate limited capacity among a portfolio of customers who remember the fill rates provided to them in the past. A customer's order quantity is positively correlated with past fill rates. Customers differ from one another in their contribution margins, their sensitivities to the past, and in their demand volatilities. By analyzing and comparing policies that ignore goodwill with ones that account for it, we investigate when and how customer memory effects impact supplier profits. We develop an approximate dynamic programming policy that dynamically rationalizes the fill rates the firm provides to each customer. This policy achieves higher rewards than margin-greedy and Lagrangian policies and yields insights into how a supplier can effectively manage customer memories to its advantage. This paper was accepted by Martin Lariviere, operations management.