## 2 Background
The research in [17] applies a Geographic Information System (GIS)-based Multi-Criteria Decision Analysis process using the analytical hierarchy process (AHP) to address the electric vehicle charging station site selection in light of 15 environmental, economic, and urbanity criteria. A hierarchical optimization model, integrating three levels of analysis, is also developed in [18] to assist city planners with charging station location selection and system design. In this work, 10 different locations were considered for EVCS deployment, consisting of shopping malls and cultural centers. A study on expanding existing EVCS networks is realized in [19], following two different expansion strategies to choose additional sites for charging station placement, with the candidate locations being based on real-world data on charging station utilization and correlation with places of common interest. Other research studies integrate into the EVCS allocation problem the optimization of its sizing by assigning the optimal charging piles per infrastructure deployed. The work in [20] examines the optimal EV charging infrastructure location and capacity determination problem, assuming charging queuing behavior with finite queue length constraints. The GA is used to minimize the total cost, which considers charging queuing behavior with finite queue length and various sitting constraints in a small-scale case study (20 EVCS candidate locations). The effort in [21] utilizes Genetic Algorithms to solve the fast-charging station location-and-sizing problem to maximize EV charging station owner profits across a region for BEV owners who wish to charge _en route_, taking into consideration elastic demand, station congestion, and network equilibrium. The research in [22] proposes a multi-criterion-oriented optimization approach to determine the optimal charging station placement and charging piles assignment under multiple constraints, such as recharging demand and cruising range, by employing two different algorithms, the Lazy Greedy with Direct Gain (LGDG) and Lazy Greedy with Effective Gain (LGEG), both based on the greedy method. Public trajectory data collected from the taxi cabs have been utilized for building up and verifying the research work based on the hypothesis that the traveler/driver behavior remains unchanged when switching to driving/taking electric vehicles. The work in [23] develops a model based on an artificial immune algorithm to identify the optimal solution considering the overall user satisfaction: charging convenience, charging cost, and charging time. In [24], the planning and sizing model is established to minimize the annual cost of the charging station under multiple constraints that consider the actual load, charging power, and charging distance through the utilization of a neighborhood mutation immune clone selection algorithm. The Analytic Hierarchy Process (AHP) is adopted in [25] to rank 10 EVCS alternative locations in a large city district by assigning relative weights as input for the mathematical model to determine the number of charging stations to install and their relevant capacities. In the context of optimal EVCS placement planning, the charging facility type is used to build up the optimization objectives in several research studies. In the study [26], three typical kinds of charging facilities have been considered: slow-charging facility (SCF), normal-charging facility (NCF), and fast-charging facility (FCF). The resulting optimization model employs mixed-integer second-order cone programming (MISOCP) and aims to minimize the annualized social cost of the whole EV charging system (investment cost of EVCSs, grid reinforcement cost, O&M cost of EVCSs, network losses cost). The research in [27] establishes a GIS-based multi-objective Particle Swarm Optimization model aiming at minimizing the total cost of charging station investment and maximizing the service coverage.