Technical Note—Joint Learning and Optimization of Multi-Product Pricing with Finite Resource Capacity and Unknown Demand Parameters
Qi (George) Chen, Stefanus Jasin, Izak Duenyas
Operations Research · 2021 · Volume 69 · Issue 2
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
marketing: pricing, inventory/production: approximation/heuristics, Market Analytics and Revenue Management, network revenue management, exploration and exploitation, parametric demand models, well-separated demand models, heuristics, asymptotic approach
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