Trip Pricing in User-Based Relocation for Station-Based Carsharing Systems

The imbalanced distribution of vehicles due to asymmetric demands has become a main operational challenge in increasingly popular carsharing services. This study investigates a user-based relocation strategy to alleviate the imbalances in widely adopted non-reserved station-based carsharing systems, in which users are induced to relocate vehicles via monetary incentives. An incentive-based trip pricing problem considering the strategic choices of users who aim to maximize the traveling utility is formulated to optimize the incentives. A bi-level mixed-integer programming model is developed and reformulated into a single-level one, with a valid inequality introduced to improve computational efficiency. An improved particle swarm optimization algorithm is designed because the problem is proved to be NP-hard. Extensive numerical results validate the mathematical formulations and the solution method. Results also indicate that the relocation strategy improves both the operating profit and the number of served users in the configuration of the experiments. Several managerial insights are provided for service operators as well.