Bi-Objective Optimization of Product Selection and Ranking Considering Sequential Search

Customer choices in online retailing are often influenced by sequential search behavior. However, most existing models ignore the dynamic property of this process. To address this gap, we study a bi-objective product selection and ranking (BP-SS) problem considering sequential search, aiming to jointly optimize the expected revenue and market share. We first develop a two-stage choice model with consideration sets to capture how sequential search influences customer decision-making. Based on this model, we formulate the BP-SS problem and analyze its structural properties, including problem reformulation, unimodality analysis and ranking rules. To solve the problem efficiently, we develop a dynamic programing-based approximation algorithm. Numerical experiments demonstrate that the algorithm consistently outperforms benchmark methods, especially in large-scale scenarios. Moreover, a moderate increase in the trade-off parameter substantially improves market share without a huge revenue loss. This study offers a novel and robust framework for product selection and ranking in online retailing, offering practical insights for balancing profitability and competitive positioning.