## 2 Related Works
### Studies on ALNS for CEVRPTW  
The CEVRPTW is solved with exact solution methods, heuristic and metaheuristic approaches, and machine learning methods. Although the methods that provide exact solutions are quite effective in small-scale problems, they cause difficulties due to excessive running time costs in large-scale problems. In machine learning methods, there are difficulties, such as data requirements and the complexity of model training processes. For this reason, metaheuristic approaches come to the fore due to their rapid applicability and flexibility. The methods used in the studies that have increased rapidly over the years are given in Figure 2.  
It is seen that metaheuristics are the most preferred methods, and the ALNS algorithm is the most used metaheuristic (Figure 2). [PERSON] and [PERSON] (2015) worked with a mixed heterogeneous fleet for the problem using ALNS. They analyzed the effect of factors such as speed, slope, and load on minimizing energy consumption [15]. [PERSON] and [PERSON] (2016) studied the CEVRPTW by considering partial charge. They defined the problem mathematically and used ALNS metaheuristics to find its solution. They showedthat using partial charge instead of full charge gives more efficient results [17]. [PERSON] and [PERSON] (2018) studied the problem and three different charging technologies using metaheuristics. The study compared the fleet size and energy costs with the charging speed [20]. [PERSON] and [PERSON] (2018) solved the problem by minimizing the energy consumption depending on the load with the ALNS algorithm [21]. [PERSON] and [PERSON] (2020) used the ALNS algorithm to solve the problem, considering nonlinear charging and load-dependent energy consumption, which allows electric vehicles to make multiple visits to charging stations [22]. [PERSON] et al. (2019) considered public charging stations and waiting in charging queues in dynamic situations using ALNS and Mixed Integer Linear Programming (MILP) methods [20]. [PERSON] and [PERSON] (2022) verified that the cost of a partial charging strategy is less than that of a full charging strategy with their proposed ALNS algorithm [27]. [PERSON] et al. (2022) considered partial charging policies, including time windows and multiple charging technologies for mixed fleets consisting of electric and internal combustion vehicles. They calculated energy consumption and emissions by considering the load factor and optimized the cost using different charging technologies at charging stations and the partial charging policy [29]. [PERSON] et al. (2023) used ALNS to solve the EVRPTW problem, including normal, fast, and ultra-fast charging strategies, and analyzed the cost effects of different charging technologies [30]. [PERSON] et al. (2023) considered the electric vehicle routing problem, including time windows, partial charges, and parcel lockers. They proposed a network design based on parcel lockers and charging stations at different locations, and each customer was assigned to only one designated parcel locker. A Mixed Integer Programming (MIP) model was constructed to solve the problem, and its performance was tested using the ALNS algorithm [31]. [PERSON] et al. (2025) considered an extended EVRPTW model, including EV delivery, self-pickup from parcel lockers (PLs), and flexible delivery methods to provide services to customers. They used the ALNS algorithm to solve the problem. They shared the results of the analysis on the effective use of parcel lockers. In the analysis, they explained that using parcel lockers can reduce customer service costs, and the cost reduction depends on factors such as customers' locations and time windows. They also observed a decrease in the number of vehicles used in certain situations [33].