In this paper, we explore the challenge of assortment planning in the context of quick commerce, a rapidly growing business model that aims to deliver time-sensitive products. In order to achieve quick delivery to satisfy the immediate demands of online customers in close proximity, personalized online assortments need to be included in brick-and-mortar store offerings. With the presence of this physical linkage requirement and distinct multinomial logit choice models for online consumer segments, the firm seeks to maximize overall revenue by selecting an optimal assortment of products for local stores and by tailoring a personalized assortment for each online consumer segment. We employ an integer programming approach to solve this NP-hard problem to global optimality. In particular, we derive convex hull results to represent the consumer choice of each online segment under a general class of operational constraints, and to characterize the relation between assortment decisions and choice probabilities of products. Our convex hull results, coupled with a modified choice probability–ordered separation algorithm, yield formulations that provide a significant computational advantage over existing methods. Finally, we illustrate how our convex hull results can be used to address other assortment optimization problems. This paper was accepted by Chung Piaw Teo, optimization. Funding: Y. Rong’s work was supported by the National Natural Science Foundation of China Grants 72025201 and 72221001. T. He’s work was supported by the National Natural Science Foundation of China Grants 72101146 and 72231003, and Y. Wang’s work was supported by the National Natural Science Foundation of China Grants 72331006. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02996 .
Chen et al. (Fri,) studied this question.