Joint Optimization of Allocations and Relocations in One-Way Carsharing Systems with Two Operators

Abstract

Multiple operators commonly coexist in one-way carsharing systems. Therefore, the performance of the system is worth exploring. We used one-way carsharing systems with two operators as an example, assuming that one joins first and is called the leader, and another is named the follower. A nonlinear mixed-integer bilevel programming model is set to jointly optimize the allocations (including the number of shared cars and parking spaces) and the relocations. The users’ preferences are included by comprehensively considering the travel cost, number of available shared cars at the departing station, and the number of parking spaces at the arrival station. Relocations are also performed in the upper-level model and the lower-level model to maximize the profits of the leader and the follower, respectively. The models of both levels connect by setting the number of parking spaces at each station and the users’ choice between operators. A customized adaptive genetic algorithm is proposed based on the characteristic of the model. Case studies in Beijing reveal that, compared to a single-operator carsharing system, the total profit and demand satisfied by shared cars increased significantly in two-operator carsharing systems, with increases of 37.59% and 56.55%, respectively. Considering the users’ preferences, the leader can meet 266.84% more demands and earn a 174.76% higher profit. As for the follower, the corresponding growth rates are 124.98% and 36.30%, respectively.