## 2 Literature Review  
The EVRP is in the NP-hard problem class. Various approaches have been employed by researchers to address this problem, including exact solution methods, heuristic methods, metaheuristic methods, and learning-based techniques. A summary of the advantages and disadvantages associated with each of these methodologies is provided in Table 1.  
The number of publications regarding the EVRP on the Web of Science (WOS) between 2014 and 2024 is shown in Figure 1. To obtain Figure 1, the WOS database was searched with the query \"ALL = \"electric vehicle\" AND (ALL = route planning OR ALL = path planning OR ALL = task scheduling OR ALL = routing)\". This query included all papers that contained the phrase \"electric vehicle\" and also contained at least one of the phrases \"route planning\", \"path planning\", \"task scheduling\", or \"routing\". It seems that interest in the subject has increased in recent years. There are review and survey studies on the EVRP and its variants in the literature [1; 30; 31; 32; 33]. [PERSON] and [PERSON] [30] present a literature review on the latest developments regarding the EVRP. This study provides an overview of state-of-the-art procedures for solving the EVRP and related problems. The study of [PERSON] et al. [31] presents comprehensive research on EV routing problems and their many types. In the study, EV routing problems are divided into nine classes. For each of these nine classes, the settings of the problem variables and the algorithms used to obtain their solutions are examined. Many researchers have used algorithms that give exact solutions (branch-and-cut algorithms, branch-and-price algorithms, branch-and-price-and-cut algorithms) for the solution of the EVRP and its variants in the literature [15; 16; 17; 18]. The study of [PERSON] et al. [15] considers four variants of the problem: (i) at most, a single charge is allowed per route, and the batteries are fully charged during the visit to a charging station; (ii) more than one charge per route (full charging only); (iii) a maximum of one single charge (partial charging). Exact branch-price-and-cut algorithms are presented to generate suitable vehicle routes. Computational results show all four variants are solvable--for instance, with up to 100 customers and 21 charging stations. [PERSON] and [PERSON] [16] discuss the EVRPTW and model this problem by allowing partial charges with three charging configurations: normal, fast, and super-fast. The study aims to minimize the total charging cost while operating a minimum number of vehicles. The problem is formulated as a Mixed Integer Linear Program (MILP), and small instances are solved using the CPLEX solver. The Adaptive Large Neighborhood Search (ALNS) is combined with an exact method to solve large instances. In the [PERSON] et al. [17] study, a branch-and-cut-and-price algorithm is proposed \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Approach** & **Advantage** & **Disadvantage** \\ \hline \multirow{3}{*}{Exact methods [15; 16; 17; 18]} & - & Gives exact solutions & - & Long solution times for \\  & - & Suitable for small and & - & large-sized problems \\  & & medium-sized problems. & - & These methods do not \\ \hline Heuristics, metaheuristics & - & Shorter solution time & - & guarantee an optimal solution \\ \hline \multirow{6}{*}{Learning-based methods} & - & Adapting to changing conditions and & \multirow{2}{*}{-} & \multirow{2}{*}{These methods do not} \\  & - & dynamic environments & & guarantee an optimal solution \\ \cline{1-1}  & - & Easily adaptable to different problem & - & The training process can be long \\ \cline{1-1}  & - & types and constraints & & and require high \\ \cline{1-1}  & - & Ability to make decisions in environments & \multirow{2}{*}{-} & computational power \\ \cline{1-1}  & - & containing uncertainty and randomness & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different solution approaches for the VRP. Figure 1: EVRP studies based on year.