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.

Multi-objective optimization problems are characterized by conflicting objectives, making it impossible to identify a single optimal solution. Instead, solution methods aim to produce a diverse set of non-dominated solutions, aka Pareto optimal solutions, each offering different tradeoffs among the objectives. Evolutionary multi-objective algorithms (EMOAs) are commonly employed to generate these varied sets of solutions. However, the abundance of solutions presents a significant challenge for decision-makers (DMs) in identifying the most preferred solution. The problem becomes even more pronounced as the number of objectives increases, requiring exponentially more computational resources and more solutions to properly represent the Pareto optimal set. Interactive EMOAs (iEMOAs) mitigate this challenge by integrating DM preferences into the optimization process to limit the search to regions of the Pareto front that are interesting to the DM. Despite their advantages, existing methods often struggle with effectively learning and utilizing DM preferences. This study investigates tree-based learning methods for preference modeling in iEMOAs by conducting a systematic comparison of decision trees (DTs) and random forests (RFs). Additionally, it examines the impact of active learning as a solution selection strategy for improving preference elicitation. Experimental results demonstrate that RF achieves significantly higher accuracy than DT in learning DM preferences. Furthermore, integrating active learning enhances preference learning within RF, further improving its accuracy. These findings highlight the potential of active learning for enhancing preference-driven optimization, offering more effective strategies for interactive multi-objective decision-making.

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.

Natural disasters have intensified in recent years, directly affecting populations, especially in urban and semiurban areas where infrastructure is damaged and causes casualties. Rapid damage assessment is essential for decision-making and developing relief measures for victims of such disasters. In the aftermath of such disasters, the application of deep learning to aerial imagery has become increasingly crucial for damage assessment. By categorizing damage levels to buildings and other environmental elements, such as roads, these methods can significantly support informed decision-making processes. RescueNet is proposed as a database for training these intelligent damage estimation models. In this study, we introduce a GA-driven hyperparameter optimization for adapting PSPNet to the RescueNet hurricane imagery dataset. Using a genetic algorithm to explore key design choices (e.g, learning rate, backbone depth, pooling bin sizes, dropout probability, …) we automatically evolve high-performance configurations over successive generations. Our best model boosts mean Intersection over Union on undamaged buildings from 95.16% to 96.57% and on vehicles from 85.97% to 89.76%, outperforming the default PSPNet settings. By sharply improving the model's ability to distinguish intact versus damaged regions, we deliver faster, more reliable disaster-impact assessments. In ongoing work, we are extending this metaheuristic approach to optimize additional critical classes such as roads to further enhance end-to-end operational readiness.

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.

This paper addresses the computationally challenging edge-based facility location problem with the objective of minimizing total travel time while accommodating uniformly distributed demand on network edges. To enhance computational efficiency, the proposed method integrates differential evolution (DE) with three distinct surrogate models: random forest, extreme learning machines, and extreme gradient boosting. While the concept of distributed demand on network edges presents a more realistic depiction of location problems, the necessity of decomposing edges and assigning them to their nearest facilities increases the complexity of the problem at hand. Therefore, the development of an effective and efficient solution method is crucial, particularly in time-sensitive contexts where rapid decisions are essential. Empirical evaluations demonstrate the efficacy and efficiency of the proposed multi-surrogate approach when compared to traditional DE and a leading surrogate-based algorithm. The results illustrate superior computational performance while preserving solution quality across various benchmark functions.

This study scrutinizes a facility location problem with uniformly distributed demands along the network edges. The objective is to determine the best locations for establishing facilities such that the aggregate traveling time is minimized. Each network edge is divided into two segments, each assigned to its closest open facility. Finding the best combination for establishing facilities and using them as a basis for decomposing network edges form the main decision variables. Due to the NP-hardness of this problem, a Genetic Algorithm is used as the optimization method. This algorithm is known as one of the best metaheuristics for solving this problem. To accelerate the optimization process considering the computationally expensive fitness evaluation of the edge-based location problems, an extreme learning machine is hybridized with the implemented genetic algorithm to serve as a surrogate model for approximating the fitness of the majority of individuals. The results obtained from solving generated instances indicate that while keeping the same quality of solutions, the developed surrogate model-based genetic algorithm significantly reduces the required computational time.

Developing healthy diets in early childhood may help determine future healthy foods. Many kids pass time in childcare, but few studies evaluated the nutritional quality of menus and snacks in childcare homes. Therefore, serving healthier meals is the main phase to attaining that objective. In spite of this, planning a healthful and balanced menu manually is complicated, wasteful, and time-consuming. The objective of this paper is to develop a Multi-period Goal Programming (MGP) model to plan menus for the Tunisian school canteen. School children aged 06 to 12 years old will have three different types of menus according to their cases (standard menus, sports menus, and diabetics menus). This model minimizes the sum of deviations of nutrient requirements for three groups of menus and for one week (Monday, Tuesday, Wednesday, Thursday, and Friday). The MGP is broken down into a number of periods, and an ideal choice needs to be made on the relationship between these decisions in each period. This model respects the budget provided by the canteen manager, it ensures assigning the recipes according to their correct category (sweet snack, salad, main meal, fruit and salty snack), it guarantees that each recipe for each menu is not used more than once per week as well as nutrients requirement constraint. As a consequence, the final dietary plan for the children is an optimal assortment of the recipes intake, related to minimizing deviations from the explained goals. The diet model develops a nutritional adviser's work and avoids errors when preparing a diet plan manually.

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.

This paper addresses the facility location problem in the context of multiple-server facilities subject to congestion. The objective is to select a subset of facilities from a pool of candidate locations in order to meet customers’ demands. Additionally, the number of servers allocated to each facility is treated as a decision variable, and the service time for each server follows an exponential distribution. As network location problems are known to be NP-hard, this study introduces a random forest as a surrogate model with differential evaluation to minimize the aggregate expected traveling times and aggregate expected waiting times of customers. The proposed algorithm is implemented and evaluated on a set of test problems with different sizes and specifications, demonstrating its high efficiency compared to differential evaluation.

This study proposes a new bi-objective mixed-integer non-linear mathematical model for an interruptible single-server congested facility location problem with uniformly distributed demands along the network edges. It is assumed that in the case of server disruption, all the waiting customers leave the facility without receiving the service, and there would be no entry until fixing the server. Limiting by the maximum waiting time threshold, this study aims to determine the number and locations of established facilities. The first objective function minimizes the facility establishment costs, while the second objective function is to minimize the aggregate traveling, waiting, and demand lost costs. Due to the NP-hardness nature of the problem, several state-of-the-art evolutionary multi-objective optimization (EMO) algorithms are applied to find the set of non-dominated solutions. The results indicate that the applied SPEA - II algorithm outperforms its competitors in the majority of generated test cases.

As one of the most prominent variants of the facility location problem, the p-median problem aims to determine the best locations for establishing p number of facilities such that the aggregate customers' transportation cost is minimized. Since the p-median problem is classified as NP-hard, the application of metaheuristics to solve it is inevitable. Considering the fast development in metaheuristics, choosing the most appropriate algorithm to solve this problem is a difficult task. Therefore, this work presents a comparative study of several classical and recently developed nature-inspired optimization algorithms to solve the discrete uncapacitated p-median problem on several randomly generated test instances with different sizes and spec-ifications.

This study proposes a novel multi-objective integer programming model for a collision-free discrete drone path planning problem. Considering the possibility of bypassing obstacles or flying above them, this study aims to minimize the path length, energy consumption, and the accumulated maximum path risk simultaneously. The static environment is represented as 3D grid cells. Due to the NP-hardness nature of the problem, several state-of-the-art evolutionary multi-objective optimization (EMO) algorithms with customized crossover and mutation operators are applied to find a set of non-dominated solutions. The results show the effectiveness of applied algorithms in solving several generated test cases.

This paper considers multiobjective UAV path planning in a real 3D environment with the objective to find a safe energy-efficient path. An Enhanced Non-dominated Sorting Genetic Algorithm-II, called ENSGA-II, is proposed and combines several sorts of heuristic information to customize crossover and mutation operators. Furthermore, a local search and a ranking-based roulette wheel selection are incorporated for the mating procedure. Experiment results confirm that ENSGA-II has a better convergence rate and spread of solutions on several new real-world datasets. The effectiveness of the local search component is also validated on the CrazyS robot operating system (ROS) package which consists of a pelican quadcopter's modeling.