Service Region Design for Urban Electric Vehicle Sharing Systems

90. He, L., Mak, H. Y., Rong, Y., & Shen, Z. J. M. (2017). Service region design for urban electric vehicle sharing systems. Manufacturing & Service Operations Management. https://doi.org/10.1287/msom.2016.0611.

Abstract: Emerging collaborative consumption business models have shown promise in terms of both generating business opportunities and enhancing the efficient use of resources. In the transportation domain, car-sharing models are being adopted on a mass scale in major metropolitan areas worldwide. This mode of servicized mobility bridges the resource efficiency of public transit and the flexibility of personal transportation. Beyond the significant potential to reduce car ownership, car sharing shows promise in supporting the adoption of fuel-efficient vehicles, such as electric vehicles (EVs), because of these vehicles’ special cost structure with high purchase but low operating costs. Recently, key players in the car-sharing business, such as Autolib’, car2go, and DriveNow, have begun to employ EVs in an operations model that accommodates one-way trips. On the one hand (and particularly in free-floating car sharing), the one-way model results in significant improvements in coverage of travel needs and therefore in adoption potential compared with the conventional round-trip-only model (advocated by Zipcar, for example). On the other hand, this model poses tremendous planning and operational challenges. In this work, we study the planning problem faced by service providers in designing a geographical service region in which to operate the service. This decision entails trade-offs between maximizing customer catchment by covering travel needs and controlling fleet operation costs. We develop a mathematical programming model that incorporates details of both customer adoption behavior and fleet management (including EV repositioning and charging) under imbalanced travel patterns. To address inherent planning uncertainty with regard to adoption patterns, we employ a distributionally robust optimization framework that informs robust decisions to overcome possible ambiguity (or lacking) of data. Mathematically, the problem can be approximated by a mixed integer second-order cone program, which is computationally tractable with practical scale data. Applying this approach to the case of car2go’s service with real operations data, we address a number of planning questions and suggest that there is potential for the future development of this service.