## 4 Review of Case Studies: Electric Vehicle Routing Optimization
Their model incorporates time window restrictions, a heterogeneous fleet with diverse travel times, costs, and capacities, order-to-vehicle compatibility constraints, orders with multiple services and pickup/delivery locations, distinct starting and ending locations for each vehicle, route restrictions, and limitations on drivers' working hours. To solve this problem, they proposed an iterative improvement approach that modifies the neighborhood structure during the search process. Computational experiments demonstrated the effectiveness of their method, as it successfully handled multiple vehicles and transportation requests. Many authors also integrated few conventional methods. For example, [PERSON] [39] proposed an original heuristic algorithm based on Tabu search to address the hybrid vehicle routing problem (HVRP) with time window constraints and nonlinear penalized delays. Their objective function considered a weighted sum of the vehicle cost, total distance traveled, and a penalty cost for delays. [PERSON] et al. [40] formulated the HVRP with time window constraints and multiple vehicle visits to the pickup and delivery nodes as a mixed-integer linear programming (MILP) model. They utilized a branch and cut algorithm-based commercial software package to obtain optimal vehicle routes. [PERSON] et al. [41] introduced an iterated local search (ILS) metaheuristic using a variable neighborhood descent procedure to find optimal solutions for various HVRP variants with different numbers of customers and fleet sizes. [PERSON] and [PERSON] [42] addressed a novel variant of HVRP, considering permissible overloads and a penalty cost for overloads below a predefined limit. They proposed a sequential insertion-based heuristic and evaluated it on [PERSON]'s benchmark datasets [43]. [PERSON] et al. [44] conducted a practical case study of a third-party logistics firm in Thailand and modeled an HVRP with time windows, multi-product deliveries, and limited vehicle and driver availability. They employed GA and branch and bound approaches to find optimal solutions. [PERSON] et al. [45] introduced a greenIRP with a heterogeneous fleet and developed a mixed-integer linear programming model to minimize the total cost, including load-dependent fuel consumption and emission costs. Numerical tests were performed to evaluate the effectiveness of their comprehensive study. [PERSON] et al. [46] focused on solving the multi-objective vehicle routing problem (VRP) with simultaneous pick-up and delivery and time window constraints (Mo-EVRPSDPTW). This study proposed a mathematical model that aims to minimize total logistics cost and maximize average customer satisfaction. To address challenges such as traffic congestion and dynamic power consumption, a multi-objective GA (NSGA-II) with improved strategies is developed. The experimental results demonstrated the effectiveness of the improved NSGA-II algorithm in reducing logistics costs while maintaining high customer satisfaction levels. The algorithm generated more reasonable vehicle service routes, meeting customer needs and optimizing the distribution process. Sensitivity analysis on traffic congestion confirms the stability and effectiveness of the algorithm. The authors stated that future research directions include considering nonlinear charging and queuing time in the charging process to account for real-world conditions. Additionally, incorporating clustering algorithms to enhance the multi-objective evolutionary algorithm's performance is suggested. [PERSON] et al. [47] investigated the electric VRP with time windows (EVRPTW) and its implications for incorporating EVs into fleet operations. The study focused on the energy consumption rate of EVs and develops a mixed-integer linear programming (MILP) model called EVRPTW-ECR to dynamically estimate the EV's maximum range based on speed and load. The model used two linearization methods to handle the nonlinear relationship between speed and travel time.