基于现场实验的酒店收益管理强化学习方法

IF 6.5 2区管理学 Q1 MANAGEMENT

Journal of Operations Management Pub Date : 2023-03-15 DOI:10.1002/joom.1246

Ji Chen, Yifan Xu, Peiwen Yu, Jun Zhang

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A reinforcement learning approach for hotel revenue management with evidence from field experiments

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来源期刊

Journal of Operations Management 管理科学-运筹学与管理科学

CiteScore

11.00

自引率

15.40%

发文量

审稿时长

24 months

期刊介绍： The Journal of Operations Management (JOM) is a leading academic publication dedicated to advancing the field of operations management (OM) through rigorous and original research. The journal's primary audience is the academic community, although it also values contributions that attract the interest of practitioners. However, it does not publish articles that are primarily aimed at practitioners, as academic relevance is a fundamental requirement. JOM focuses on the management aspects of various types of operations, including manufacturing, service, and supply chain operations. The journal's scope is broad, covering both profit-oriented and non-profit organizations. The core criterion for publication is that the research question must be centered around operations management, rather than merely using operations as a context. For instance, a study on charismatic leadership in a manufacturing setting would only be within JOM's scope if it directly relates to the management of operations; the mere setting of the study is not enough. Published papers in JOM are expected to address real-world operational questions and challenges. While not all research must be driven by practical concerns, there must be a credible link to practice that is considered from the outset of the research, not as an afterthought. Authors are cautioned against assuming that academic knowledge can be easily translated into practical applications without proper justification. JOM's articles are abstracted and indexed by several prestigious databases and services, including Engineering Information, Inc.; Executive Sciences Institute; INSPEC; International Abstracts in Operations Research; Cambridge Scientific Abstracts; SciSearch/Science Citation Index; CompuMath Citation Index; Current Contents/Engineering, Computing & Technology; Information Access Company; and Social Sciences Citation Index. This ensures that the journal's research is widely accessible and recognized within the academic and professional communities.