The implementation of a sustainable and efficient electric bus (eBus) transportation network requires addressing multiple concerns, such as limited driving range and battery charging/discharging time. Currently, eBuses can travel between 200 to 300 km on a single charge, and fast charging stations can fully recharge a battery in a matter of minutes. However, a failure in a charging station might negatively impact the operation of the system with unnecessary delays for the users. Taking this into account, we propose and implement a model for the Robust eBuses Charging Location problem that takes into account potential vulnerabilities of the transportation system. Our model incorporates a protection mechanism that allows eBuses to reach a backup charging station in case the regular one is down. We propose a MIP model to tackle this problem with minimal disruptions in the regular operation of the eBuses. Furthermore, we also present a Large Neighbourhood Search framework to efficiently tackle the problem. Our empirical evaluation suggests that our framework can operate a robust service with a small number of charging stations for three Irish cities and our Large Neighbourhood Search approach largely outperforms a popular commercial MIP solver.

Electric buses (eBuses) will be the mainstream in mass urban transportation in the near future. Thus, installing the charging infrastructure in convenient locations will play a critical role in the transition to eBuses. Taking this into account, in this paper we propose an iterated local search algorithm to optimize the location of charging stations while satisfying certain properties of the transportation system, e.g., satisfying the demand and ensuring that the limited driving range of the buses will not impact the service. The effectiveness of our approach is demonstrated by experimenting with a set of problem instances with real data from 3 Irish cities, i.e., Limerick, Cork, and Dublin. We compare our approach against a MIP-based solution. Results show that our approach is superior in terms of scalability and its anytime behavior.