When Location Shapes Choice: Placement Optimization of Substitutable Products
研究了在实体店或线上平台中，如何优化可替代产品在展示位置的布局以最大化期望收益，提出了一个通用随机算法，能对任意浏览分布和选择模型给出近似最优解。
Strategic product placement can have a strong influence on customer purchase behavior in physical stores as well as online platforms. Motivated by this, we consider the problem of optimizing the placement of substitutable products in designated display locations to maximize the expected revenue of the seller. We model the customer behavior as a two-stage process: first, the customer visits a subset of display locations according to a browsing distribution; and second, the customer chooses at most one product from the displayed products at those locations according to a choice model. Our goal is to design a general algorithm that can select and place the products optimally for any browsing distribution and choice model, and we call this the Placement problem. We give a randomized algorithm that utilizes an [Formula: see text]-approximate algorithm for cardinality-constrained assortment optimization and outputs a [Formula: see text]-approximate solution (in expectation) for Placement with m display locations—that is, our algorithm outputs a solution with value at least [Formula: see text] factor of the optimal—and this is tight in the worst case. We also give algorithms with stronger guarantees in some special cases. In particular, we give an efficient deterministic [Formula: see text]-approximation algorithm for the Markov choice model and a tight [Formula: see text]-approximation algorithm for the problem when products have identical prices. This paper was accepted by Omar Besbes, revenue management and market analytics. Funding: O. El Housni received financial support from the National Science Foundation (NSF) Division of Civil, Mechanical, and Manufacturing Innovation [Grant 2226900]. R. Udwani received financial support from the NSF Division of Civil, Mechanical, and Manufacturing Innovation [Grant 2340306]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03826 .