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研究院论坛:NO.22A Reinforcement Learning Approach for Hotel Revenue Management
2019年12月03日

报告题目:A reinforcement learning approach for hotel revenue management

报 告 人:陈吉

报告时间:2019年12月3日,10:30-11:30

报告地点:博学楼I206

主办单位:现代供应链管理研究院


【报告人简介】

陈吉,博士毕业于复旦大学管理学院。博士期间曾前往新加坡管理大学访问交流。随后在香港科技大学运营管理系任职博士后。主要研究方向为运营管理、数据驱动的酒店收益管理、排队论、医疗服务系统等。

 

【摘要】

We develop a new data-driven approach for hotel revenue management. In this approach, the dynamic capacity allocation problem for multiple class customers is solved in two steps: First, a recommended average price is computed, and then the capacity allocation decision is made based on the average price. The recommended average price is computed with a reinforcement learning algorithm. The capacity allocation decision is made based on a linear programming model taking into account a hotel’s preference for different classes of customers.

 

Our approach is implemented in five randomly selected hotels of a hotel chain to assess its effectiveness. The difference-in-differences-in-differences estimate with random assignment and matching shows that our approach improves the key operational performance measure–Revenue Per Available Room–by 11.66% with an associated 90% confidence interval of [6.40%, 16.92%], without significantly affecting the average occupancy rate. Since July 1st, 2017, all hotels of the chain have been using our approach for capacity allocation decisions.

 

Our approach focuses on the revenue improvement rather than revenue optimization, which is aligned with a hotel manager’s incentives. In addition, our recommended solution can be presented in a two-dimension figure, which helps ease a manager’s resistance to a new data-driven approach. Most importantly, our suggested solution is aligned with the long-term goal of the hotel chain, because our approach has been shown to be able to improve hotel revenue without changing the hotel occupancy rate.

 

单位:现代供应链管理研究院

审核人:张颖

撰稿人:田鑫



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