Technical note - Joint learning and optimization of multi-product pricing with finite resource capacity and unknown demand parameters

Chen, Q, Jasin, S and Duenyas, I (2021) Technical note - Joint learning and optimization of multi-product pricing with finite resource capacity and unknown demand parameters. Operations Research, 69 (2). pp. 560-573. ISSN 0030-364X OPEN ACCESS

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.

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Item Type: Article
Subject Areas: Management Science and Operations
Additional Information:

© 2021 INFORMS

Date Deposited: 09 Nov 2020 10:18
Date of first compliant deposit: 06 Nov 2020
Subjects: P > Pricing
D > Demand functions
N > Network analysis
Last Modified: 04 Apr 2021 04:08
URI: https://lbsresearch.london.edu/id/eprint/1543
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