## 3 Description of the ESOGU-CEVRPTW Dataset
### Dataset Generation
\[\left[\mathbf{\alpha}\times\frac{\sum(\mathbf{t_{i}}+\mathbf{s_{i}})}{\mathbf{k}},\mathbf{\beta} \times\frac{\sum(\mathbf{t_{i}}+\mathbf{s_{i}})}{\mathbf{k}}\right] \tag{1}\]  
When the formula is applied for 118 customers and (\(\alpha\), \(\beta\)) = (0, 1.2), the time window is determined as [0, 1789]. This value [0, 1789] represents a narrow time window. \"Travel time\" refers to the time it takes for the vehicle to traverse the distance between the depot and the customer. The Google Distance Matrix API is utilized to calculate this time. The Distance Matrix API can compute travel distance and duration for specific start and end points. Actual distance is used in these calculations instead of Euclidean distance. \"Service time\" corresponds to the total time required for a person to deliver the goods to the customer via a pedestrian route after the vehicle reaches the nearest point to the customer, and for the person to return to the vehicle. Figure 6: Customer clustering using the K-Means Algorithm. \"Demand\" for each customer in the problem is generated from a discrete uniform distribution between [19; 95]. The dataset includes a distance matrix that contains the shortest distances between all customers and charging stations on the ESOGU campus. The distance matrix is generated using SUMO. By utilizing these networks, the shortest path from one node (depot, customer, or charging station) to another node is calculated. Figure 7 presents a comparison between the shortest paths computed using actual distances and Euclidean distances. As Euclidean distance does not accurately represent true travel distances, it fails to capture the complexities of real-world scenarios. The proposed dataset addresses this need by incorporating features such as buildings, streets, and other relevant infrastructure, thereby enabling route planning in a realistic environment with multiple attributes. This dataset, designed for real-world conditions, facilitates the application of advanced logistics methods. It provides a valuable resource for researchers, particularly those employing approaches that rely on environmental information, such as road characteristics (e.g., one-way, two-way, inclined, narrow), traffic regulations, and other factors. Moreover, the dataset offers the capability to simulate scenarios, such as road closures due to traffic accidents, allowing for the modeling of such situations in deep learning models. Figure 8 provides a description of the instances in the ESOGU-CEVRPTW dataset. It includes information on the number of customers in each instance, the distribution of the customers, and the time window applied. The generated dataset consists of real data from the ESOGU campus, with the campus map modeled in the SUMO simulation environment. An example problem from the dataset is presented in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Point ID** & **Location** & \begin{tabular}{c} **Earliest Start Time** \\ **to Service** \\ \end{tabular} & \begin{tabular}{c} **Latest Start Time** \\ **to Service** \\ \end{tabular} & \
\begin{tabular}{c} **Service Time** \\ **to Service** \\ \end{tabular} &
\begin{tabular}{c} **Demand** \\ \end{tabular} \\ \hline
32 & 39.752487–30.488123 & 1322 & 1378 & 180 & 76 \\
19 & 39.748319–30.489034 & 296 & 350 & 120 & 57 \\
31 & 39.752941–30.483072 & 1372 & 1448 & 120 & 57 \\
60A/2 & 39.749343–30.474854 & 764 & 840 & 240 & 38 \\
75 & 39.746589–30.474760 & 424 & 487 & 120 & 38 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: An instance of the ESOGU-CEVRPTW dataset. Figure 7: Comparison of Euclidean distance and actual distance. Each location in the instance has an identifier (id), latitude and longitude, earliest start time to service, latest start time to service, demand and service time. In the dataset presented within the paper, both the time window (TW) and without time window versions of the CEVRP are solved using a mathematical model to obtain optimal solutions.