## 4 Review of Case Studies: Electric Vehicle Routing Optimization
\begin{table}
\begin{tabular}{c c c c c} \hline
**Study** & **Application Domain** & **Optimization Technique** & **Performance Metrics** & **Results** \\ \hline \multirow{4}{*}{[PERSON] et al. [47]} & \multirow{4}{*}{Electric VRP with time windows (EVRPTW)} & \multirow{4}{*}{MILP} & \multirow{4}{*}{Energy consumption rate, EV range} & MILP model optimally solves EVRPTW instances with up to 25 customers, heuristic approach used for larger instances, highlighting the importance of considering EV characteristics in route planning. \\ \cline{3-5}  & & & Robust optimization model efficiently addresses EVRPTW with uncertain customer demand and weight-related energy consumption, supporting practical and efficient distribution tasks. \\ \hline \multirow{4}{*}{[PERSON] and [PERSON] [49]} & \multirow{4}{*}{Electric VRP with time windows and mixed fleet (EVRPTWMF)} & \multirow{4}{*}{Adaptive large neighborhood search algorithm} & \multirow{4}{*}{Energy consumption, routing costs} & \multirow{4}{*}{Energy consumption, routing costs} \\  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [51]} & \multirow{2}{*}{Two-stage electric VRP (2 sFRP)} & \multirow{2}{*}{Two-stage process, detailed energy cost estimation} & \multirow{2}{*}{Time, energy estimations} & Two-stage process provides accurate time and energy estimators for optimizing electric vehicle routes. \\ \cline{3-5}  & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._Table 2 presents a critical summary of various studies addressing GA-based optimization techniques for electric VRPs. The studies cover a range of application domains such as personal EV routing [30], inventory routing [22], EV navigation, and the distribution of perishable goods [37], among others. Multiple optimization techniques, including GAs, metaheuristic algorithms, and mathematical programming models, are employed to solve these problems. Overall, the studies demonstrate the effectiveness of the proposed optimization techniques in improving various performance metrics such as travel time, recharging costs, fuel consumption, pollutant emissions, total electricity consumption, logistics cost, and customer satisfaction [38; 40]. The results indicate that the optimization approaches provide valuable insights and contribute to decision making in VRPs. Some studies highlight the importance of incorporating real-life factors such as traffic delays, detour distances, variable pricing, and terrain grades into the optimization models [30; 35]. These factors significantly impact the performance of EVs and the energy consumption of the fleet. By considering these factors, the studies demonstrate improved optimization results and better alignment with real-world conditions. Comparatively, the studies utilize different optimization techniques and approaches, each tailored to the specific problem at hand. GAs and metaheuristic algorithms like particle swarm optimization, Tabu search, and adaptive large neighborhood search are frequently employed to efficiently explore the solution space and find optimal or near-optimal solutions. These techniques often outperform traditional methods like exact algorithms in terms of solution quality and computational efficiency [27; 45; 52]. Furthermore, some studies address multi-objective VRPs, where conflicting objectives such as minimizing the number of vehicles and minimizing the total distance traveled need to be balanced [48; 51; 53]. The adoption of multi-objective optimization frameworks, such as Pareto ranking and non-dominated sorting, allows for the generation of a set of solutions representing different trade-offs between objectives. This flexibility enables decision makers to select solutions based on specific priorities and constraints, enhancing the practicality and effectiveness of the optimization process. It is worth noting that several studies propose future research directions to further enhance the optimization models and techniques [8; 46; 49; 53].