## 2 Background  
To improve EV charging efficiency, researchers have been working on the Electric Vehicle Charging Station (EVCS) distribution problem, investigating different perspectives in order to reach an optimal allocation scheme. In terms of planning scenarios, several factors and targets have been considered, including the optimal location, number, service type, and capacity of each EVCS to satisfy the user's needs and ultimately the growth of EV penetration. In terms of solution algorithms, the global research effort has developed a variety of methods and algorithms in order to solve the charging station sizing and placement problem [7]. In particular, the formulated optimization problems for the placement of EVCS can form a single or multi-objective, linear or nonlinear, convex or concave assembly. According to the used variables, the formulated problem can be continuous, integer, discrete, or combinatory [8]. The most frequently used algorithms in existing studies can be divided into two types: heuristic/meta-heuristic and mathematical algorithms. Heuristic algorithms are relatively more popular than mathematical ones due to their ability to find a global or near-global optimum solution even in complex problems. Several studies have attempted to estimate the optimal location of the EVCS by examining alternative distribution scenarios based on a predefined set of candidate EVCS sites. The work in [9] develops a bi-level mathematical model to optimize the location of charging stations for EVs with consideration of the driving range. The upper level is to maximize the flows served by charging stations, while the lower level depicts route choice behavior given the location of the charging station. Genetic Algorithms have also been employed in [10] and [11] to calculate the necessary number of charging stations and best placement positions to satisfy the clients' demand using origin-destination (OD) data of conventional vehicles and real-world driving data of 196 battery EVs. The research in [12] uses an intelligent multi-objective optimization method to handle the problem by integrating a multi-objective particle swarm optimization (MOPSO) process to obtain a set of Pareto optimal solutions and an entropy weight method-based evaluation process to select the final solution from the Pareto optimal solutions. The effort in [13] develops an optimization model of charging station location that considers the waiting time for EVs in the queue for charging as an influencing factor. The minimization of the time cost to EV drivers is performed with the utilization of the hybrid evolutionary algorithm SCE-UA. Another work in [14] presents a case study for planning the locations of public electric vehicle charging stations with the employment of three different classic facility location models (set covering model, maximal covering location model, and \(p\)-median model) to test the model's effectiveness. The optimal positioning of EVCS in an urban area is analyzedin [15] by introducing weighting maps (cost values, distance) for managing different social requirements into the optimization process while utilizing evolutionary algorithms (Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Biogeography-based Optimization (BBO), and Social Network Optimization (SNO)). The proposed solution to the deployment problem applies a greedy approach that consists of placing all the CS, one at a time, such that each one is locally optimal and is tested through various scenarios with a different predefined number of EVCS to appoint. A multi-period optimization problem is proposed in [16] for EVCS placement, in which the distribution of charging demand is modeled with a combination of node-based and flow-based approaches, so as to model the needs of EVs to recharge on intermediary stops on long-haul travels. For this problem, a mixed-integer linear programming (MILP) formulation is considered.