Innovative Approaches for Electric Vehicles Relocation in Sharing Systems

This article presents two methods for solving the electric vehicles (EVs) relocation in EV-sharing system: 1) a centralized method where the decisions are taken by a unique decision-maker by using the complete knowledge of the system and 2) a randomized matheuristic algorithm where decisions are taken by the stations that coordinate for solving the relocation problem. For each methodology, two approaches are proposed for the EV relocation, i.e., the relocation performed by the EV-sharing operators and the relocation involving registered users also with an incentive scheme based on the crowdsourcing concept. In both the methods, two integer linear programming (ILP) problems are formulated to minimize the relocation cost in the two considered approaches. Moreover, in the randomized matheuristic method, a set of smart stations solve local ILP problems to produce a relocation plan. Finally, some instances and a case study are presented to demonstrate the effectiveness of the proposed approaches for the EVs relocation problem.
Note to Practitioners
—This article is motivated by the need to optimize the relocation process in the electric vehicle (EV)-sharing systems in order to minimize the relocation costs and guarantee the high quality of the service. To this aim, we first propose a centralized optimization that can be applied by the EV-sharing company for incentivizing users to optimally relocate vehicles in the stations. In this context, both the users and the company obtain benefits. Second, the randomized matheuristic optimization allows the stations to reach a decision about the relocation plan by using local information. The presented strategies can be applied in real applications, and in particular, the randomized matheuristic approach appears a promising strategy for large systems by using limited resources with low computational effort. Future research will focus on the EVs relocation problem in free-floating sharing systems.