## 4 Review of Case Studies: Electric Vehicle Routing Optimization
Furthermore, when considering the number of vehicles and distance, the GA-TS method combined with fuzzy logic yields better results, with a shorter mileage and a smaller number of vehicles, indicating the effectiveness of this approach in optimizing the E-VRPTW problem. They concluded that the integration of fuzzy logic and the GA-TS approach enhances the E-VRPTW by providing more optimal routes. The utilization of fuzzy logic in time windows introduces flexibility in arrival times, leading to improved fitness values. The comparison of results with other studies confirms the effectiveness of the GA-TS method and fuzzy logic, resulting in shorter distances traveled and a reduced number of required vehicles. [PERSON] and [PERSON] [35] focused on optimizing the capacitated vehicle routing problem with time windows (CVRPTW) for a mineral water company distributor. The study utilized GA, Tabu Search Algorithm (TS), and a hybrid approach combining both algorithms. The objective is to reduce the total distance traveled and determine the optimal number of homogeneous fleet vehicles. The results show that the hybrid GA-Tabu Search approach yields the best solution, reducing the existing route's total distance by 15.99%. The travel distances achieved were 932.47 km for GA, 982.96 km for TS, and 789.67 km for the hybrid algorithm. This study suggested considering customer time windows and evaluating the impact on transportation costs for future research. Additionally, visualizing the results can aid in route determination and further enhance the optimization process. Similarly, [PERSON] et al. [36] focused on optimizing the shuttle bus services at UNITEN by implementing the capacitated vehicle routing problem (CVRP) model. This study utilized a GA as a metaheuristic approach to improve transportation efficiency and reduce costs. The GA is effective in solving complex computational problems, such as VRP, and has successfully achieved the study goals by minimizing distances and improving route planning. However, the limitations of the algorithm are identified, including the simplicity of the research problem with a single variable (distance) and a limited number of bus stops. The lack of alternative physical roads and the constraint of passing through all predetermined locations present challenges in finding shorter routes. The study suggested future work to expand the algorithm's domain to include more diverse locations, which would showcase its strength and performance in handling complex scenarios. The authors concluded that the implementation of the CVRP model using a GA has proven beneficial in optimizing shuttle bus services, reducing transportation costs, and improving efficiency at UNITEN. However, [PERSON] et al. [37] addressed the problem of distributing perishable goods with quality constraints using a heterogeneous fleet of vehicles. A mathematical programming model is proposed to optimize the distribution process, considering factors such as time windows and deterioration rates. The model proved effective in handling perishable products with short shelf lives. A GA-based heuristic is introduced to efficiently determine optimal routes. The results from the proposed model aligned with expectations, requiring more vehicles as constraints on quality and time become more stringent. The heuristic provided computationally efficient solutions with minimal loss in solution quality. The authors stated that future research could explore incorporating traffic congestion, demand variability, and the distribution of multiple items, as well as addressing pickup operations in addition to drop-off operations at retail stores. [PERSON] and [PERSON] [38] proposed a general vehicle routing problem (GVRP) model that addresses various complexities encountered in real-life applications.