Efficient Scheduling of Electric Vehicle Charging via Tabu Search and Exact Optimization Techniques

The growing adoption of electric vehicles (EVs) necessitates efficient scheduling of charging operations to optimize limited infrastructure and maximize demand satisfaction. This study addresses the Electric Vehicle Charging Scheduling Problem (EVCSP) with the objective of maximizing the number of fulfilled charging requests. We propose two complementary solution frameworks. The first is an enhanced global mathematical programming model that captures the full problem structure and delivers high-quality solutions, even for large-scale instances. The second is a hybrid optimization approach that decomposes the problem into two interrelated components: EV-to-charger assignment and energy delivery optimization. A tabu search (TS) algorithm explores diverse assignment configurations, while each generated assignment is evaluated via exact mathematical programming to optimize energy allocation. Computational experiments show that both frameworks significantly outperform conventional approaches in solution quality and efficiency. These results highlight the potential of the proposed methods for scalable and effective EV charging management, supporting the operational needs of increasingly complex EV networks.