Entropic-Based Robust Vehicle Rental Revenue Management with Substitution and Repositioning

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 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 with the expectation optimization benchmark model. Compared with the sample average approximation model, which disregards the variant nature of probability transitions, the model can achieve higher revenue with fewer vehicle inventories. Besides, in contrast to repositioning, our model is inclined to enhance the system’s revenue robustness by increasing the substitution ratio.

History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis.

Funding: C. Fu is supported by the National Natural Science Foundation of China [Grants 72401229, 72310107003, 72271201]. N. Zhu is supported by the National Natural Science Foundation of China [Grant 72471163] and the Humanities and Social Science Fund of Ministry of Education of China [Grant 25JZD038].

Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.1086) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2024.1086). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/.