Dynamic pricing of hotel rooms based on reinforcement learning with unknown demand distribution

被引：0

作者：

Zhu H. ^{[1
]}

Zhang M. ^{[1
]}

Tang J. ^{[1
]}

机构：

[1] School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian

来源：

Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2023年 / 43卷 / 02期

基金：

中国国家自然科学基金;

关键词：

dynamic pricing; reinforcement learning; revenue management; SARSA(λ) algorithm;

D O I：

10.12011/SETP2022-1705

中图分类号：

学科分类号：

摘要：

Traditional hotel dynamic pricing research always considers improving demand forecasting methods or considers that the demand environment is known, while the demand distribution in real life is usually unknown. In this paper, we established a multi-period dynamic pricing model for hotel rooms based on Markov decision process with unknown demand distribution, and used the reinforcement learning method to propose improved algorithms based on SARSA(λ) to solve the dynamic pricing model of rooms. In order to improve the solving ability and convergence speed of the algorithm, we proposed the ε-SARSA(λ) algorithm based on the improved ε-greedy strategy and the ISA-SARSA(λ) algorithm based on the improved simulated annealing strategy. Through numerical experiments, the revenue optimization results of the four algorithms, SARSA(λ), ε-SARSA(λ), SA-SARSA(λ) and ISA-SARSA(λ), were compared. The study results verify the effectiveness of improved algorithms and show that the ISA-SARSA(λ) algorithm has the best solution performance. © 2023 Systems Engineering Society of China. All rights reserved.

引用

页码：509 / 523

页数：14

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