## 4 Review of Case Studies: Electric Vehicle Routing Optimization
Computational experiments demonstrated that the MILP model can be optimally solved for instances with up to 25 customers using the CPLEX solver and partially optimized for larger instances using a heuristic approach. The research highlighted the importance of considering EV characteristics and energy consumption in route planning to support greener transportation. [PERSON] et al. [48] addressed the electric VRP with time windows (EVRPTW) by considering uncertain customer demand and weight-related energy consumption of EVs. A robust optimization model based on a route-related uncertain set is proposed, and an adaptive large neighborhood search heuristic is developed to solve the problem. The effectiveness of the method is verified through experiments, highlighting the importance of considering uncertainty and energy consumption in EVRPTW for practical and efficient distribution tasks. [PERSON] and [PERSON] [49] presented the electric VRP with time windows and mixed fleet (E-VRPTWMF), which focuses on optimizing the routing of a combination of electric commercial vehicles (ECVs) and conventional internal combustion commercial vehicles (ICCVs). This study incorporated a realistic energy consumption model that considers factors such as speed, gradient, and cargo load distribution, which are crucial for ECVs due to their limited driving range and recharging requirements. An adaptive large neighborhood search algorithm, combined with local search, is developed to solve the problem efficiently. The paper conducted numerical studies on new test instances to analyze the impact of load distribution on solution quality and structure. It also explores different objective functions and evaluates the contribution of ECVs to overall routing costs. The benchmark instances of related problems are used to demonstrate the algorithm's performance. Overall, the paper provides insights and solutions for optimizing routing in mixed fleets of ECVs and ICCVs. A study by [PERSON] et al. [50] examined the energy optimization for autonomous urban electric buses with regenerative braking using GA, specifically addressing the challenge of extending the range of these buses on a single battery charge. This study is conducted within the Sohjoa Baltic project, which aims to improve public transportation through the use of autonomous electric minibuses in various Baltic Sea region cities. The authors introduced an extension of the speed profile optimization (SPO) framework to the context of urban electric minibuses with regenerative braking. The SPO methodology is employed to optimize the speed profiles of the buses, taking into account the frequent stops and short distances between them. [PERSON] et al. [51] presented the two-stage electric VRP (2 sEVRP), which addresses the challenge of accurately predicting energy consumption and planning charging stops for fleets of electric commercial vehicles. The proposed approach incorporated detailed factors such as topography and speed profiles to estimate energy costs. It utilized a two-stage process to determine optimal paths and routes, considering battery and time-window constraints. The method demonstrated improved accuracy in time and energy estimations compared to existing approaches. Numerical experiments conducted on a road network in Gothenburg, Sweden, validate the effectiveness of the proposed method. The results indicated that the planned routes are feasible in terms of energy demand and include appropriate charging stops when required. Table 1 provides a summary of the key parameters and contributions of each paper, allowing for a comparative analysis of the problem addressed, objectives, optimization approach, and key contributions of each study. These case studies demonstrate the effectiveness of GAs in optimizing routing for EVs. By considering multiple objectives such as energy consumption, travel time, and customer satisfaction, the GA-based approaches offer promising results in improving route efficiency and sustainability.