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Abstract:
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We consider the dynamic pricing problem where an online retailer aims to maximize revenue over the course of a selling season given limited inventory. As common in practice, the retailer does not know the expected demand at each price and must learn the demand information from clickstream data. We propose an implementable and effective dynamic pricing algorithm, which builds upon the Thompson sampling algorithm used for multi-armed bandit problems by incorporating inventory constraints into the pricing decisions. Our algorithm proves to have both strong theoretical performance guarantees as well as promising numerical performance results when compared to other algorithms developed for the same setting. More broadly, our paper contributes to the literature on the multi-armed bandit problem with resource constraints, since our algorithm applies directly to this setting when the inventory constraints are interpreted as general resource constraints.
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