## 4 Review of Case Studies: Electric Vehicle Routing Optimization
Simulation results demonstrated the effectiveness of both the ICS and SCC algorithms, with the SCC algorithm significantly reducing the computation complexity while maintaining an acceptable level of deviation from the optimal cost. [PERSON] et al. [33] proposed a novel approach to solving the VRP with time windows (VRPTW) by treating it as a multi-objective optimization problem (MOP) using a GA with Pareto ranking. Unlike previous approaches that biased the optimization towards minimizing the number of vehicles, this MOP formulation considered the number of vehicles and the total distance traveled as separate dimensions in the search space. This avoided introducing search bias and allows for a more comprehensive exploration of the solution space. The proposed approach recognized the inherent nature of the VRPTW as a MOP and provides a computationally advantageous solution. Experimental results demonstrated the competitiveness of the approach compared to existing methods, highlighting the effectiveness of the MOP formulation in capturing alternative solutions that prioritize different objectives. By adopting the MOP framework, the authors addressed the philosophical and theoretical aspects of the VRPTW, acknowledging that there are multiple trade-offs between minimizing the number of vehicles and minimizing the total distance traveled. The proposed approach does not prioritize one objective over the other but allows for the generation of a set of solutions that represent different trade-offs between the two objectives. This MOP formulation provides flexibility for decision makers to select solutions based on their specific priorities and constraints. The computational advantages of the approach are also highlighted, as the performance and results obtained using the GA with Pareto ranking are competitive with existing methods. Another study by [PERSON] et al. [7] also focused on the electric vehicle routing problem with time windows (EVRPTW) in the context of logistics transportation. The study takes into account the terrain grades of roads, which impact the travel process of EVs, and the battery state-of-charge (SoC) dynamics are used to model the EVs' electricity consumption. The objective is to minimize the total electricity consumption while efficiently serving a set of customers with specific time windows. The problem is formulated as a mixed-integer programming model, and a hybrid GA that combines the 2-opt algorithm with GA is developed to address the EVRPTW. Simulation results demonstrated that the proposed approach outperforms the simulated annealing (SA) algorithm and GA in terms of finding better solutions within a shorter computation time. The research contributes to optimizing EV routing and energy consumption in logistics transportation, taking into account real-world factors such as terrain grades and battery dynamics. However, [PERSON] and [PERSON] [34] addressed the EV routing problem with time windows (E-VRPTW) and proposed an improvement by incorporating Fuzzy Logic using the GA and Tabu Search (TS) methods. They determined the value of fitness of the current population based on four indicators, \(travel\_cost\), \(fuel\_cost\), \(time\_cost\), and \(vehicle\_cost\) as per Equation (6). \[\centering Fitness=\frac{1}{\sum Travel\_cost+\sum Fuel\_cost+\sum Time\_cost+ \sum Vehicle\_cost}\@add@centering \tag{6}\]  
The addition of fuzzy logic introduced tolerance in the time windows, allowing for flexibility in arrival times. The experimental results show that the proposed E-VRPTFW model with GA-TS and fuzzy logic achieves higher fitness values compared to the E-VRPTW  
Figure 3: Distribution of GA-based EV routing optimization studies. model without fuzzy logic. The fitness values increase by an average of 14.39% in the first trial and 8.49% in the second trial.