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

Qi (George) Chen (), Stefanus Jasin () and Izak Duenyas ()
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Qi (George) Chen: London Business School, Regent’s Park, London NW1 4SA, United Kingdom;
Stefanus Jasin: Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan 75080
Izak Duenyas: Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan 75080

Operations Research, 2021, vol. 69, issue 2, 560-573

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 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.

Keywords: network revenue management; exploration and exploitation; parametric demand models; well-separated demand models; heuristics; asymptotic approach (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:69:y:2021:i:2:p:560-573

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