Joint Inventory Allocation and Assortment Personalization with Performance Guarantees

12/8/22 | 4:15pm | E51-145


 

 

 

 

Huseyin Topaloglu

Howard and Eleanor Morgan Professor
Cornell


Abstract: In this talk, we give approximation algorithms for a joint inventory allocation and assortment personalization problem motivated by an online retail setting. In our problem, we have a limited amount of storage capacity that needs to be allocated among multiple products to serve customers that arrive over a selling horizon. At the beginning of the selling horizon, we decide how many units of each product to stock. Over the selling horizon, customers arrive at the platform one by one to make a purchase. Based on the remaining inventories of the products and the information available on the arriving customer, we offer a personalized assortment of products to each customer. The customer either makes a choice within the offered assortment or leaves without a purchase. Our goal is to decide how many units of each product to stock at the beginning of the selling horizon and to find a policy to figure out which personalized assortment to offer to each arriving customer to maximize the total expected revenue over the selling horizon. Our problem is motivated by same-day-delivery applications in online retail, where the retailer needs to allocate the limited storage capacity in an urban warehouse among different variants in a product category, while having the capability of offering personalized assortments to customers to make better use of remaining inventories. Allocating the storage capacity among the products requires tackling a combinatorial problem, whereas finding an assortment personalization policy requires approximating a dynamic program with a high-dimensional state variable. When the choices of the customers are governed by the multinomial logit model, we give a constant-factor approximation algorithm for this joint inventory allocation and assortment personalization problem. Under a general choice model, we give an algorithm that is asymptotically optimal as the storage capacity gets large. In the latter result, the demand can be scaled in an arbitrary fashion along with the storage capacity. This is joint work with Yicheng Bai, Omar El Housni and Paat Rusmevichientong.

Bio: Huseyin Topaloglu is the Howard and Eleanor Morgan Professor in the School of Operations Research and Information Engineering at Cornell Tech. He got his B.Sc. in Industrial Engineering from Bogazici University of Turkey and his Ph.D. in Operations Research and Financial Engineering from Princeton University. He has been a faculty member at the School of Operations Research and Information Engineering since 2002. He is currently serving as the Program Director for the Master of Engineering Program in Operations Research and Information Engineering at Cornell Tech. Professor Topaloglu works on large-scale stochastic optimization problems that arise in areas such as revenue management, inventory control, transportation logistics, and supply chain management. He is the department editor for Revenue Management Department of Product and Operations Management Journal. He serves as an associate editor for the journals Management Science, Mathematical Programming C, Naval Research Logistics, Operations Research, Transportation Science, IIE Transactions, and Surveys in Operations Research and Management Science.

Event Time: 

2022 - 16:15