Technical note - Joint learning and optimization of multi-product pricing with finite resource capacity and unknown demand parameters
Journal
Operations Research
Subject
Management Science and Operations
Publishing details
Authors / Editors
Chen Q;Jasin S;Duenyas I
Biographies
Publication Year
2021
Abstract
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 a 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 a NRM with a well-separated parametric demand model and a continuum set of feasible price vectors.
Keywords
Network revenue management; Exploration and exploitation; Parametric demand models; Well-separated demand models; Heuristics; Asymptotic approach
Available on ECCH
No
