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

• 成果类型: Article
• 署名作者: Chen, Qi (George); Jasin, Stefanus; Duenyas, Izak
• 署名单位: University of London; London Business School; University of Michigan System; University of Michigan
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2020.2078
• 发表日期: 2021
• 页码: 560-573
• 关键词: Network Revenue Management; Exploration and exploitation; parametric demand models; well-separated demand models; heuristics; asymptotic approach

摘要

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.