## 4 Review of Case Studies: Electric Vehicle Routing Optimization
The integration of real-time data and dynamic constraints further enhances the performance of the routing optimization process for EVs. To evaluate the efficiency and quality of the GA-based approach for multi-objective message routing in EVs, several performance evaluation metrics have been utilized in previous studies. These metrics offer valuable insights into the routing solutions achieved by the GA in terms of their effectiveness and efficiency. Figure 4 depicts the performance metrics utilized in various studies examined in this research. It is evident that the majority of researchers concentrated on optimizing two key factors: energy consumption and distance traveled. These metrics highlight the significance of enhancing energy efficiency and minimizing the overall distance covered for electric vehicle (EV) routing optimization. By prioritizing these objectives, researchers aim to achieve greener and more sustainable transportation solutions for EVs. However, it is important to note that different studies might consider additional performance metrics to address specific requirements and real-world constraints in their respective EV routing scenarios. Table 2 summarizes GA studies for optimizing electric vehicle routing-based applications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Problem Addressed** & **Objective(s)** & **Optimization Approach** & **Key Contributions** \\ \hline [PERSON] et al. [30] & Personal electric VRP (pEVRP) & Minimize travel time and recharging costs & \(e\)-constraint method, GA, PSO & Incorporation of real-life factors, such as traffic delays and variable pricing, in the optimization model. \\ \hline [PERSON] and [PERSON] [22] & IRP & Optimize fuel consumption and pollutant emissions & Multi-objective quantum algorithm & Consideration of fleet composition and comparison of performance with other methods. \\ \hline [PERSON] et al. [32] & Navigation system optimization for EVs & Optimize routing and charging policies & Improved Chrono-SFT (SC) & Incorporation of real-life pricing policy and reduction in computational complexity. \\ \hline [PERSON] et al. [53] & VRP with time windows (VRPTW) & Multi-objective optimization using GA & GA with Pareto ranking & Treating VRPTW is a multi-objective problem, considering number of vehicles and total distance as separate dimensions in the search space. \\ \hline [PERSON] et al. [7] & Electric VRP with time windows (EVRPTW) & Minimize total electricity & Mixed-integer linear programming model, Hybrid GA & Consideration of terrain grades and battery state-of-charge dynamics in the model. \\ \hline [PERSON] and [PERSON] [34] & Electric VRP with time windows (E-VRPTW) & Optimize routes with fuzzy logic and time windows & GA, Tabu search (TS), fuzzy logic & Introduction of tolerance in time windows using fuzzy logic, improved fitness values. \\ \hline [PERSON] and [PERSON] [58] & Capacitated VRP with time windows (CVRPTW) & Minimize total distance traveled & GA, Tabu search algorithm (TS), Hybrid approach & Comparison of algorithms and reduction in total distance traveled using the hybrid approach. \\ \hline [PERSON] et al. [56] & Optimization of shuttle bus services & Minimize distances and improve route planning & GA & Application of GA to the capacitated VRP (CVRP) in the context of shuttle bus services. \\ \hline [PERSON] et al. [37] & Distribution of perishable goods with quality constraints & Optimize distribution process considering time windows and deterioration rates & Mathematical programming model, genetic algorithm-based heuristic & Optimization of perishable goods distribution considering quality constraints. \\ \hline [PERSON] and [PERSON] [38] & General VRP (GVRP) & Optimize vehicle routes considering various complexities & Iterative improvement approach & Handling of multiple vehicles, diverse travel times, order-to-vehicle compatibility, and other constraints. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of electric vehicle routing optimization papers.