Technical Note—Joint Learning and Optimization of Multi-Product Pricing with Finite Resource Capacity and Unknown Demand Parameters.

Dynamic Pricing with Limited Resource and Unknown Demand Parameters When the underlying demand model's parameters are unknown, how should a seller adjust prices of multiple products which are subject to finite resource constraints? In "Technical Note – Joint Learning and Optimization of Multi-Product Pricing with Finite Resource Capacity and Unknown Demand Parameters," Chen, Jasin, and Duenyas revisit the classic learning and earning problem in the context of network revenue management with a continuum of feasible prices and develop two computationally efficient pricing heuristics. For the general parametric demand models, their first heuristic attains an asymptotic regret bound which exactly matches the known theoretical lower bound under any feasible pricing control. If the underlying demand model family also satisfies a separable structure, their second heuristic leverages this structural property to attain a much better regret. The key design features underpinning the two heuristics could be powerful ideas for developing effective pricing policies in other applications. We consider joint learning and pricing in network revenue management (NRM) with multiple products, multiple resources with finite capacity, parametric demand model, and a continuum set of feasible price vectors. We study the setting with a general parametric demand model and the setting with a well-separated demand model. For the general parametric demand model, we propose a heuristic that is rate-optimal (i.e., its regret bound exactly matches the known theoretical lower bound under any feasible pricing control for our setting). This heuristic is the first rate-optimal heuristic for an NRM with a general parametric demand model and a continuum of feasible price vectors. For the well-separated demand model, we propose a heuristic that is close to rate-optimal (up to a multiplicative logarithmic term). Our second heuristic is the first in the literature that deals with the setting of an NRM with a well-separated parametric demand model and a continuum set of feasible price vectors. [ABSTRACT FROM AUTHOR]