Optimizing Algorithms to Allocate Electric Vehicles Based on Charger Types and User Preferences
Abstract
Electric vehicles (EVs) are crucial for mitigating greenhouse gas emissions in urban transportation. However, their integration requires efficient charging infrastructure and allocation strategies. In this paper, five heuristic algorithms were developed to allocate EVs to urban charging stations. This allocation process incorporates critical constraints, such as user preferences and charger type compatibility, while respecting station capacities governed by power output rules. The proposed methods include four initial allocation heuristics, ranging from capacity-centric and nearest-neighbor approaches to random assignments, complemented by a local search algorithm for solution refinement. To evaluate these heuristics, an optimization model minimizing station establishment and vehicle travel costs was adapted from the literature. Computational experiments were performed on both synthetic instances and real-world case studies. The results indicate that the developed heuristics, especially when enhanced by local search, deliver high-quality, near-optimal solutions within highly competitive computational times. Consequently, this study offers a scalable decision-support tool for urban planners, demonstrating how the joint optimization of infrastructure costs and user preferences can foster sustainable urban mobility and accelerate EV adoption. Ultimately, these findings offer actionable insights for scaling heterogeneous EV infrastructure, fostering urban sustainability and mitigating transport-related carbon emissions.