One-way station-based electric carsharing service design considering route selection based on road congestion

In the field of one-way station-based electric carsharing systems (OSECSs), determining the optimal service design is a major challenge faced by operators. To address this issue, This study proposes a simulation-optimisation framework that takes into account several factors, such as fleet size, station capacity, rental pricing, vehicle relocation, and staff rebalancing to determine the optimal service design. Specially, the simulation module of this framework is used to evaluates the performance of different carsharing service designs by taking into account the selection of optimal routes based on road congestion, nonlinear charging profiles, and elastic demand influenced by price and travel time. In the optimisation module of this framework, the study introduces a novel greedy genetic search algorithm to solve the optimisation problem. The proposed simulation-optimisation framework is tested on a carsharing network with 15 stations in Chengdu, China, and the numerical experiment shows its efficiency. Additionally, the study analyzes the impact of different degrees of demand and travel time variation, demand scenarios, and road congestion scenarios on the OSECS. By doing so, it provides a more comprehensive understanding of the system and the factors that affect its performance.