A Primal–Dual Learning Algorithm for Personalized Dynamic Pricing with an Inventory Constraint

Ningyuan Chen () and Guillermo Gallego ()
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Ningyuan Chen: Rotman School of Management, University of Toronto, Toronto, Ontario M5S 1A1, Canada
Guillermo Gallego: Department of Industrial Engineering & Decision Analytics, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong

Mathematics of Operations Research, 2022, vol. 47, issue 4, 2585-2613

Abstract: We consider the problem of a firm seeking to use personalized pricing to sell an exogenously given stock of a product over a finite selling horizon to different consumer types. We assume that the type of an arriving consumer can be observed, but the demand function associated with each type is initially unknown. The firm sets personalized prices dynamically for each type and attempts to maximize the revenue over the season. We provide a learning algorithm that is near optimal when the demand and capacity scale in proportion. The algorithm utilizes the primal–dual formulation of the problem and learns the dual optimal solution explicitly. It allows the algorithm to overcome the curse of dimensionality (the rate of regret is independent of the number of types) and sheds light on novel algorithmic designs for learning problems with resource constraints.

Keywords: Primary: 90B05; 90C39; network revenue management; multi-armed bandit; learning and earning; dynamic pricing; online retailing (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormoor:v:47:y:2022:i:4:p:2585-2613

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