Topic: Entropic-based robust vehicle rental revenue management with substitution and repositioning
Time: 9:30 PM-10:30PM, Oct. 20, 2025
Location: Room A1148, New Main Building
Guest: Prof. Zhu Ning is a Professor at the School of Management, University of Science and Technology of China (USTC). His main research areas include transportation system operation management and optimization, robust optimization, and mixed-integer optimization.He has published over 50 papers, some of which appear in high-level journals in the fields of transportation management and management science, such as TS、TR-Part A/B（9 papers）/C/E、M&SOM、POMS、IJOC、EJOR、Journal of Scheduling、Journal of Management Sciences in China, and Systems Engineering - Theory & Practice. He has presided over 5 projects funded by the National Natural Science Foundation of China (including Young Scientists Fund Project and General Program) and 2 projects funded by the Ministry of Education. He has won awards such as the Excellent Paper Award at the 14th International Workshop on Computational Transportation Science, and the First Prize of the Management Science Practice Award at the 16th International Annual Conference of Chinese Scholars in Management Science and Engineering.He serves as a Guest Editor of Transportation Research Part B, Associate Editor of Journal of Transportation Engineering and Information, and Member of the 2nd Transportation Management Sub-Committee of the Chinese Society of Management Science and Engineering.
Abstract:
This paper investigates the practical inventory management challenges faced by the vehicle rental industry, which arise from the random arrival of orders and lead times before demand occurs, as well as the uncertainty and dynamic nature of cancellations and rental durations after demand is realized. To ensure on-demand rental services while mitigating the impact of randomness on inventory stability, the industry usually employs substitution service and inventory repositioning. We extend the methodology of Bandi et al. (2018) and propose a tractable robust approach featuring an entropic risk measure. This approach jointly optimizes substitution service and inventory repositioning, addressing the maximization of revenue under varied risk-aversion preferences while safeguarding against multiple uncertainties and misspecification. Numerous numerical experiments constructed by real-world data exhibit that our model achieves 16% higher average weekly revenues compared to the expectation optimization benchmark model. Compared to the sample average approximation model, which disregards the variant nature of probability transitions, the model can achieve higher revenue with fewer vehicle inventories. Besides, we also find that further restricting substitution to implement only when the order is placed can reduce the conservatism of our model, further improving the out-of-sample revenue and enhancing the interpretability of the substitution service.
