A Ranking-Based Optimization Algorithm for the Vehicle Relocation Problem in Car Sharing Services
Piotr Szwed, Paweł Skrzynski, Jarosław Wąs
TL;DR
The paper tackles the Vehicle Relocation Problem in free-floating car-sharing by introducing a zone-based framework that simplifies the urban area into a few dozen coherent zones. It pairs inter-zone relocation with a fast ranking-based algorithm that leverages predicted vehicle availability, demand probability, and travel times, enabling real-time, scooter-assisted relocations. The approach is validated against baselines and a full MIP model using real-world Kraków data, showing improvements of approximately 8.44% over a random baseline and up to 19.6% relative to the MIP upper bound under identical resources, while remaining computationally scalable for deployment. The work demonstrates the practicality of zone-driven, decision-fast relocation in FFCS, with potential extensions to incorporate electric vehicles, dynamic pricing, and reinforcement-learning-based enhancements for future deployment.
Abstract
The paper addresses the Vehicle Relocation Problem in free-floating car-sharing services by presenting a solution focused on strategies for repositioning vehicles and transferring personnel with the use of scooters. Our method begins by dividing the service area into zones that group regions with similar temporal patterns of vehicle presence and service demand, allowing the application of discrete optimization methods. In the next stage, we propose a fast ranking-based algorithm that makes its decisions on the basis of the number of cars available in each zone, the projected probability density of demand, and estimated trip durations. The experiments were carried out on the basis of real-world data originating from a major car-sharing service operator in Poland. The results of this algorithm are evaluated against scenarios without optimization that constitute a baseline and compared with the results of an exact algorithm to solve the Mixed Integer Programming (MIP) model. As performance metrics, the total travel time was used. Under identical conditions (number of vehicles, staff, and demand distribution), the average improvements with respect to the baseline of our algorithm and MIP solver were equal to 8.44\% and 19.6\% correspondingly. However, it should be noted that the MIP model also mimicked decisions on trip selection, which are excluded by current services business rules. The analysis of results suggests that, depending on the size of the workforce, the application of the proposed solution allows for improving performance metrics by roughly 3%-10%.