## 4 Experiment and Results  
In this section, we describe a case study where the mathematical model was used and the results obtained in the different scenarios. The university has 11 faculties; each faculty has a warehouse; if a department needs a product, generally it goes to the central warehouse of the university. Currently, the distribution is manual; for the experiments, 29 departments are also considered, considering a total of 40 delivery points on campus and clients were classified into five instances, the low number of clients was considered as 4, 8 and 12 clients; the average number of clients was 20 and the high number of clients between 30 and 40. The results were obtained from the mathematical model; therefore, no real deliveries were made. Table 1 shows the UAVs and truck for experimentation. These data were used in the model, the four types of UAVs have different characteristics but all are autonomous UAVs. The Universidad Autonoma de Nuevo Leon (UANL) uses 120 types of products for satisfying each faculty and department; 95% of these products are small products, 3.3% are medium products and the rest are big products; in this case, we used a random demand. In Section 1, it was mentioned that Amazon considers that 86% of it is products can be sent by drones; according to the packages that are in the UANL, this percentage is similar to the Amazon company emphasizing that most of the packages are fit for this technology. The model was executed in different approaches, to determine its feasibility in the distribution process with the incursion of the UAV with the traditional method. The experiments were performed in a CPLEX GAMS computer package with version 25.1.3 on a Mac Air with an Intel Core i5 1.4 GHz processor and a 4 GB memory. In Figure 2, the three approaches that were handled in the experiments are shown according to the allocation of the vehicles to satisfy each client; in this case, they are faculties and departments of the UANL. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Transportation**} & \multicolumn{1}{c}{**Maximum Load**} & \multicolumn{1}{c}{**M\%imum Distance**} \\ \hline \multirow{4}{*}{UAVs} & Matternet M2 & 2 kg & 20 km \\  & Amazon dron & 2.3 kg & 16 km \\  & Geodrone & 3 kg & 20 km \\  & Jingdong dron & 15 kg & 25 km \\ \hline Truck & Renault Kangoo & 750 kg & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Transport modes. Source: based on [30]. The first approach (Figure 2a) is the traditional approach in which a delivery truck visits all customers, but a second scenario (Figure 2b) can be observed taking into account an emerging technology. This is known as a modern approach, where delivery UAVs visit all customers and, finally, you can have a combination called a mixed modern approach Figure 2c, where UAVs and delivery trucks visit all customers. We analyzed the route cost of the traditional approach (only truck) with the modern approach and the mixed approach. We also compares the routes of these approaches and how they impact the use of UAVs when used with the truck. For the case study, seven scenarios were considered according to the sizes of the packages described in Table 2; each client instance was executed 12 times using the three approaches to determine the savings. Figure 3 shows the result of a rout emphasizing the difference between the traditional approach and the modern approach. In the figure, we can see that the first route needs to use the road and \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multicolumn{2}{c}{**Size Packages**} \\ \cline{2-3}  & **Small** & **Medium** & **Big** \\ \hline \hline \multirow{2}{*}{Scenario 1} & \multirow{2}{*}{Scenario 2} & \multirow{2}{*}{Scenario 3} & \multirow{2}{*}{Scenario 4} \\ \cline{2-3}  & & & \\ \hline \hline \multirow{2}{*}{Scenario 5} & \multirow{2}{*}{Scenario 6} & \multirow{2}{*}{Scenario 7} & \multirow{2}{*}{Scenario 8} \\ \cline{2-3}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Package Category.