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

Yuyang Tan, Hao Gong and Chunxiang Guo

SAGE Open, 2025, vol. 15, issue 3, 21582440251357057

Abstract: 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.

Keywords: online retailing; sequential search; product selection; choice model (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:15:y:2025:i:3:p:21582440251357057

DOI: 10.1177/21582440251357057

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