A Multi-station Electric Vehicle Charging Scheduling Problem with Non-identical Chargers

The growing adoption of electric vehicles (EVs) demands efficient scheduling of charging resources, particularly in multi-station networks with heterogeneous chargers and limited grid capacities. This study tackles the Multi-Station Electric Vehicle Charging Scheduling Problem (EVCSP), aiming to maximize satisfied charging demands while adhering to operational constraints. A novel mathematical model is proposed, extending single-station frameworks to accurately represent multi-station complexities. To address the scalability challenges of large instances, a hybrid memetic algorithm is developed, integrating evolutionary optimization with mathematical programming for energy allocation at individual stations. Computational experiments reveal that the mathematical model is highly effective for small-scale instances, providing optimal solutions efficiently. For larger scenarios, the hybrid memetic algorithm outperforms the model in scalability and solution quality, demonstrating its robustness for real-world applications. The proposed approaches advance the optimization of EV charging networks, offering scalable and practical solutions for modern transportation systems.