报告题目：Car Rental Revenue Management
报告人：庞湛 普渡大学
报告时间：2023年3月1日(周三)，10:00-11:30
报告地点：中国科学院大学中关村校区教学楼S406
Zoom ID：859 7012 2664 入会密码：80087
内容摘要
We We consider car rental revenue management (RM) problem, accounting for the key operational characteristics of car rental services such as the varying length of rentals and mobility of inventories which imply the inter-temporal and spatial correlations of rental demands for inventories across different locations and days.Such a system can be viewed as a generalization of many common service delivery systems such as airline network,hotel, outpatient appointment, etc.We formulate the problem as an infinite-horizon cyclic stochastic dynamic program to account for the time-varying and cyclic nature of car rental businesses. To tackle the curse of dimensionality, we propose a Lagrangian relaxation (LR) approach with product- and time-dependent Lagrangian multipliers to decomposing the dynamic network problem into multiple single station single-day sub-problems. We show that the Lagrangian dual problem is a convex program and then develop a subgradient-based algorithm to solve the dual problem and derive an LR-based bid price policy. To improve the scalability of the LR approach, we further propose three simpler LR-based bid price policy variants with either location-dependent or leadtime-dependent Lagrangian multipliers, or both. Our numerical study indicates that the LR-based bid price policies can outperform some commonly used heuristics. Using a set of real-world booking data, we provide a case study in which we empirically demonstrate the operational characteristics of car rental services, calibrate the arrival process of booking requests using a Poisson regression model and demonstrate that the LR-based bid price policies indeed outperform other heuristics consistently in both in-sample and out-of-sample horizons.
主讲人简介
Dr. Zhan Pang is Lewis B. Cullman Rising Star Professor of Supply Chain and Operations Management at Denials School of Business at Purdue University.His research interests include statistical learning and decision theory, healthcare delivery systems, supply chain risk management, and pricing and revenue management. He is a senior editor for Production and Operations Management and a founding editor of Journal of Blockchain Research. He had extensive industry experiences as an entrepreneur and a management consultant. He is a board member of a public energy technology company and a non-profit home healthcare service provider, and serves as an Innovation and Entrepreneurship Fellow at Purdue. He is actively recruiting PhD students and welcoming visiting students and scholars with strong mathematics background and research interest in statistical learning and optimization and their applications in supply chain and operations management.
