2024/05/23 by Shunnosuke Ikeda, Naoki Nishimura, Ikeda, Shunnosuke +3 · 1 citation
Business, Management and Accounting · Engineering · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Color Science and Applications #Computer Science and Game Theory (cs.GT) #Consumer Market Behavior and Pricing #FOS: Computer and information sciences #FOS: Mathematics #Industrial Vision Systems and Defect Detection #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.2405.14909
openalex publication_date 2024/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This study addresses the interpretable estimation of price bounds in the context of price optimization. In recent years, price-optimization methods have become indispensable for maximizing revenue and profits. However, effective application of these methods to real-world pricing operations remains a significant challenge. It is crucial for operators responsible for setting prices to utilize reasonable price bounds that are not only interpretable but also acceptable. Despite this necessity, most studies assume that price bounds are given constant values, and few have explored reasonable determinations of these bounds. Therefore, we propose a comprehensive framework for determining price bounds that includes both the estimation and adjustment of these bounds. Specifically, we first estimate price bounds using three distinct approaches based on historical pricing data. Then, we adjust the estimated price bounds by solving an optimization problem that incorporates shape constraints. This method allows the implementation of price optimization under practical and reasonable price bounds suitable for real-world applications. We report the effectiveness of our proposed method through numerical experiments using historical pricing data from actual services.