Recently, Professor Fu Chenyi from the School of Management, serving as co‑corresponding author, has published online the research paper entitled "Entropic‑Based Robust Vehicle Rental Revenue Management with Substitution and Repositioning" in INFORMS Journal on Computing. His co‑authors include PhD candidate Meng Zhu from Tianjin University and Professor Ning Zhu from the University of Science and Technology of China. INFORMS Journal on Computing is a top journal in operations research and one of the 24 leading academic journals for business schools (UTD‑24) selected by the University of Texas at Dallas.

(Link: https://pubsonline.informs.org/doi/10.1287/ijoc.2024.1086)

This paper investigates practical inventory‑management challenges confronting the vehicle‑rental industry. Such challenges stem from stochastic order arrivals and advance‑booking lead‑times prior to demand realization, together with uncertainty and dynamic fluctuations in order cancellations and rental durations after demands materialize. To guarantee on‑demand rental service quality while mitigating the adverse impacts of randomness on inventory stability, vehicle‑type substitution and inventory repositioning are widely adopted operational measures in the industry. To tackle these issues, this study develops an entropy‑risk‑measure‑based robust optimization approach. The method jointly optimizes vehicle‑substitution and inventory‑repositioning decisions, maximizing revenue under diverse risk‑aversion preferences while coping with multiple uncertainties and model distribution misspecification. Extensive numerical experiments built upon real‑world data show that the proposed model yields a 16‑percent increase in average weekly revenue compared with the expectation‑optimization benchmark. In contrast to the sample‑average‑approximation model, which ignores the dynamic nature of probability transitions, this model achieves higher revenue with fewer vehicle inventories. Furthermore, rather than relying heavily on repositioning alone, the model tends to strengthen the revenue robustness of the whole system by raising the substitution ratio.

About the speaker:Fu Chenyi is Professor and Doctoral Supervisor in the Department of Management Science and Engineering, School of Management. His research interests cover robust optimization and urban traffic operation optimization. He has 17 published journal papers as first or corresponding author, among which five papers appear in UTD‑24 top management‑science journals: Operations Research, Production and Operations Management, Manufacturing & Service Operations Management, and INFORMS Journal on Computing. He has published six papers in Transportation Research Part B, a leading journal in transportation operations research, and three papers in European Journal of Operational Research, a high‑level journal for operations optimization. He has received the Best Paper Award and Outstanding Doctoral Dissertation Award from the International Symposium on Computational Transportation Science. He is a recipient of the Shaanxi Sanqin Talents Introduction Program (Youth Project) and the 2025 Doctoral Innovation Incentive Program of the Chinese Systems Engineering Society. He currently presides over the NSFC Youth Project (Category‑C) and one Fundamental Research Funds for the Central Universities (Social Sciences) Project.

(Written by:Discipline & Scientific Research Office Reviewed by:Jia Ming)