Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/325775 
Year of Publication: 
2023
Citation: 
[Journal:] Operations Research Perspectives [ISSN:] 2214-7160 [Volume:] 11 [Article No.:] 100290 [Year:] 2023 [Pages:] 1-9
Publisher: 
Elsevier, Amsterdam
Abstract: 
This paper is concerned with prescriptive price optimization, which integrates machine learning models into price optimization to maximize future revenues or profits of multiple items. The prescriptive price optimization requires accurate demand forecasting models because the prediction accuracy of these models has a direct impact on price optimization aimed at increasing revenues and profits. The goal of this paper is to establish a novel framework of prescriptive price optimization using optimal regression trees, which can achieve high prediction accuracy without losing interpretability by means of mixed-integer optimization (MIO) techniques. We use the optimal regression trees for demand forecasting and then formulate the associated price optimization problem as a mixed-integer linear optimization (MILO) problem. We also develop a scalable heuristic algorithm based on the randomized coordinate ascent for efficient price optimization. Simulation results demonstrate the effectiveness of our method for price optimization and the computational efficiency of the heuristic algorithm.
Subjects: 
Coordinate ascent
Demand forecasting
Mixed-integer optimization
Price optimization
Regression tree
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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