The efficient management of electric vehicle (EV) charging infrastructure is critical to meeting the growing demand for sustainable transportation. This study addresses the Electric Vehicle Charging Scheduling Problem (EVCSP), focusing on maximizing the number of satisfied charging demands. A bi-level optimization framework is developed, with the upper-level problem solved using two approaches: a classical Genetic Algorithm (GA) and a Hybrid Genetic Algorithm (HGA) enhanced with reinforcement learning via Q-learning. The HGA incorporates a Q-table to dynamically guide mutation decisions, balancing exploration and exploitation for improved performance. At the lower level, energy allocation is optimized using a mathematical programming model, ensuring feasibility and compliance with grid and charger constraints. Computational results demonstrate the HGA’s superior ability to handle large-scale instances due to its integration of adaptive learning and optimal energy allocation. The proposed framework advances EV charging management by combining evolutionary algorithms and reinforcement learning to address the complexities of real-world charging scenarios.

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.

Electric vehicles (EVs) are pivotal to reducing greenhouse gas emissions, yet their growing adoption presents major challenges for power grid management. This paper addresses the NP-hard problem of scheduling EV charging at public stations, considering vehicles’ arrival/departure times and user-defined charging demands. Under power and charger constraints, the goal is to minimize the total deviation between requested and achieved state-of-charge levels. We propose a hybrid optimization framework that integrates a genetic algorithm with mathematical programming. To reduce computational cost, we further introduce a multi-surrogate-assisted approach that approximates the mathematical model. Simulation results demonstrate the effectiveness and efficiency of the proposed methods in solving large-scale EV charging scheduling problems.

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.

This study addresses the Electric Vehicle Charging Scheduling Problem (EVCSP) with the objective of maximizing the number of scheduled charging requests while satisfying grid and charger constraints. To solve this problem, a hybrid bi-level optimization framework is developed, where a Graph Convolutional Network (GCN) guides the upper-level assignment of EVs to chargers, and exact methods ensure feasible energy allocation at the lower level. The proposed framework is evaluated on diverse and large-scale instances, with comparisons to a simulated annealing algorithm introduced in the literature as an effective method for solving the studied problem. Results demonstrate that the hybrid GCN-based approach achieves high-quality solutions with improved computational efficiency. These findings underscore the effectiveness of integrating learned graph representations with mathematical programming to solve complex and large-scale EV charging scheduling problems.

In this paper, we present an improved methodology for scheduling electric vehicle (EV) charging at a single charging station, taking into account vehicle arrival and departure times, as well as drivers' charging requirements. The objective is to minimize the difference between desired and final state-of-charge levels while adhering to constraints on power capacity and charger availability. As electric vehicles increasingly contribute to mitigating greenhouse gas emissions, efficient charging strategies become crucial to manage their impact on the electrical grid. Our study delves into the complex task of scheduling EV charging at public stations, where drivers pre-communicate their charging needs. This study proposes improved solution methods, including a standalone mathematical programming model for scheduling, a hybrid heuristic algorithm for assignment, which combines mathematical modeling techniques for evaluation and energy allocation, and a hybrid tabu search algorithm, which uses the same mathematical model employed in the heuristic. Our results demonstrate the effectiveness of our approach in tackling the challenges of EV charging scheduling, highlighting its relevance for sustainable energy management.

This study delves into the electric vehicle charging scheduling problem within a public charging service station. The scheduling task entails managing charging demands from drivers, including arrival and departure times, current battery state-of-charge, and desired state-of-charge at departure. The scheduler must determine whether to accept or reject charging requests based on charger availability and the maximum grid capacity of the station. The primary objective is to minimize the cumulative discrepancy between the desired and final state-of-charge levels for all electric vehicles. To address this challenge, we introduce a novel architecture that harnesses the capabilities of population-based meta-heuristics as a promising method for identifying near-optimal solutions.

This paper presents a novel bi-level optimization approach for addressing the 3D truck loading problem, incorporating considerations of axle weights and items with diverse delivery time windows, as defined in the 2022 EURO/ROADEF Challenge. The main goal is to optimize the efficient stacking and allocation of items into appropriate trucks, to minimize both inventory costs and transportation expenses, which encompass the additional costs associated with employing extra trucks. The first level focuses on optimizing the assignment of items to appropriate trucks, considering factors such as weight limits and compatibility. The second level initiates using an efficient heuristic for generating stacks. Using a combination of a developed Tabu Search algorithm with multiple heuristics, the generated stacks are placed within the assigned trucks such that the unused space is minimized. The computational results demonstrate the advantage of our proposal results compared to the best-known results from the EURO/ROADEF challenge, considering identical system specifications and computational times.