## 3 Description of the ESOGU-CEVRPTW Dataset
### Dataset Generation  
This section provides a detailed explanation of the dataset generation process. The generated dataset comprises real-world data, in contrast to those commonly found in the literature. The locations of the depot, customers, and charging stations within the dataset are based on geographical locations at the Eskisehir Osmangazi University Meselik Campus (Figure 3). The map in Figure 3 is generated using Google Maps, with geographical markers in different colors added to the specified points. In Figure 3, the blue markers represent the delivery customers, and the green markers represent the charging stations. When generating the dataset, the road network of the ESOGU campus is transferred to the SUMO simulation environment. Information about all the buildings on the ESOGU campus is added to the SUMO. To display the buildings on the campus on the map, a container stop is added to the entry point of all buildings. The representation of the ESOGU campus in the SUMO Netedit environment is shown in Figure 4. In Figure 4, the blue container stop shows the pickup or delivery customers, and the green rectangles show the charging stations. Figure 3: Geographical locations of the depot, customers, and charging stations on the ESOGU campus in Eskisehir. The dataset contains instances of different sizes, including 5, 10, 20, 40, and 60 customers. The customers have been selected in three different distributions, depending on the geographical distribution of the customer locations. These distributions are the random customer distribution (R), the clustered customer distribution (C), and the customer distribution (RC) which is a mixture of both. Figure 4(a) illustrates the scenario where 20 customers are randomly selected from the 118 customers on campus for random customer distribution. Subsequently, 20 customers are selected from the customer clusters. Figure 4(b) depicts the clustered distribution of these 20 customers. By merging both types of customer distributions, a random-clustered customer distribution is created, as shown in Figure 4(c). Figure 4: ESOG�� Map in the Netedit Environment. Figure 5: Example of random, clustered, and random–clustered customer distribution in ESOGU. For the clustered customer distribution, the customers within the ESOGU campus have been divided into clusters using the K-Means algorithm. Figure 6 illustrates the customers segmented into these clusters. Following the identification of customers in the dataset, 10 parking spaces on the campus are designated as charging stations. The parameters in this dataset are generated to reflect real-world problems. To generate the time window constraint in the dataset, \"the earliest start time to service\" and \"the latest start time to service\" times for each customer are generated. The time window to start to service is generated by adapting the time window derivation mechanism from study [35], originally used for the machine scheduling problem, to the Vehicle Routing Problem. The dataset includes three different time window horizons: narrow, medium, and wide. A narrow time window requires more vehicles to serve all customers compared to a wide time window. Time windows, \"the earliest start time to service\" and \"the latest start time to service\", are derived from a discrete uniform distribution using the formula presented in Equation (1). \(\mathbf{t_{i}}\) refers to the travel time from the depot to the customer at normal speed, \(\mathbf{s_{i}}\) denotes the duration of the pickup/delivery task for the customer (including walking times to and from the vehicle as well as pickup/delivery times), and \(\mathbf{k}\) represents the number of vehicles. A wide time window, a medium time window, and a narrow time window are determined using the (\(\mathbf{\alpha}\), \(\beta\)) values used in the study by [35]. For example, a narrow time window is obtained by choosing (\(\mathbf{\alpha}\), \(\beta\)) = (0, 1.2).