Coordinated Inventory Stocking and Assortment Customization

• 成果类型: Article; Early Access
• 署名作者: Bai, Yicheng; El Housni, Omar; Rusmevichientong, Paat; Topaloglu, Huseyin
• 署名单位: University of Southern California
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2022.0651
• 发表日期: 2025
• 关键词: revenue management; CHOICE; optimization; algorithms; decisions; MODEL

摘要

We study a joint inventory stocking and assortment customization problem. We have access to a set of products that can be used to stock a storage facility with limited capacity. At the beginning of the selling horizon, we decide how many units of each product to stock. Customers of different types with type-dependent preferences for the products arrive over the selling horizon. Depending on the remaining product inventories and the type of the customer, we offer a product assortment to the arriving customer. The customer makes a choice within the assortment according to a choice model based on her type. Our goal is to choose the stocking quantities at the beginning of the selling horizon and to find a policy to offer an assortment to the customer type arriving at each time period so that we maximize the total expected revenue over the selling horizon. Our work is motivated by online platforms making same-day delivery promises or selling groceries, which require operating out of a capacity-constrained urban warehouse to be close to customers but allow offering a different assortment based on some knowledge of the type of each customer. Finding a good assortment customization policy requires approximating a highdimensional dynamic program with a state variable that keeps track of the remaining inventories. Making the stocking decisions requires solving an optimization problem that involves the value functions of the dynamic program in the objective function. We give an approximation framework for the joint inventory stocking and assortment customization problem. Using our framework, we obtain a 1 (1- 1)-approximate solution when the cus4 e tomers choose under the multinomial logit model. Under a general choice model, letting n be the number of products and K be the total number of units we can stock, we give a (1- (root ffififfi+ 1) ffifinffi 3 2 p )-approximate solution, which is asymptotically optimal for large storage K capacity. Our computational experiments on synthetically generated data sets, as well as on a real-world supermarket data set, show that our approximation framework performs well against both upper bounds on the optimal performance and other possible heuristics.