# DMRVR: Dynamic Milk-Run Vehicle Routing Solution Using Fog-Based Vehicular Ad Hoc Networks
## 4 Evaluation of the DMRVR Solution
In the application case, it was assumed that the logistics operator has one basic pickup vehicle in operation, plus one auxiliary vehicle, and eight suppliers to attend the OEM company requisites. A 3 h period has been set as the OEM upper limit to perform all the collecting tasks and returning back to the OEM depot. Table 5 show the solution parameters, which were obtained with the data presented in [10; 11]. The eight suppliers and the logistics operator were randomly spread out over the Luxembourg city area, as shown in Figure 7. Nine RSUs were also distributed in the area, eight within the suppliers' lots, and one in the logistics operator premises. RSUs located at suppliers were s \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline MAC Layer & 802.11p \\ Bit rate & 6 Mbps \\ Transmission power & 200 mW \\ Transmission range & 1000 m \\ Beacon transmission rate & 3 s \\ Beacon size & 36 bytes \\ Size of the receiving area & 500 m \\ Message forward interval & 10 s \\ Max message forward interval & 60 s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Network parameters. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Pickup vehicles & 1 \\ Auxiliary pickup vehicles & 1 \\ Suppliers & 8 \\ JIT limit & 180 min \\ Vehicle load time & normal(3 + 0.6 nGoods, 0.1) \\ Supplier tardiness limit & 15 min \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Solution parameters. nodes. In order to cope with low coverage of the nine RSUs in part of the scenarios, two more RSUs were inserted to enlarge coverage. Their locations were set out empirically. Table 6 presents some pickup route characteristics, including the collected cargo quantity at each supplier, the probability of tardiness occurrence, and the expected tardiness values. The last OEM line in Table 6 reflects the total delivered product load at the OEM production line. Four different vehicle densities were considered in the model, covering situations from sparse to dense scenarios. This was done to reflect the dynamic pickup vehicle routing efficiency, as well as the impacts on the vehicular network. The adopted vehicle density intervals in the simulation were: 175-600; 303-1200; 500-3900, and 716-4600 vehicles, respectively. Considering the extended modeling area of the LuST, and the high number of vehicles in dense scenarios, the resulting simulation times reached more than 190 h in some cases. That situation, with computation times that are too long, was a limiting factor during the trials. Table 7 depicts the observed simulation times for all analyzed scenarios. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Supplier**} & \multicolumn{2}{c}{**Cargo Amount**} & \multicolumn{1}{c}{**Tardiness**} & \multirow{2}{*}{**Delay (min)**} \\  & **(m3)** & & & \\ \hline
1 & 1.09 & 0.06 & 7.0 \\
2 & 0.35 & 0.02 & 7.0 \\
3 & 0.75 & 0.16 & 25.0 \\
4 & 5.56 & 0.08 & 13.0 \\
5 & 7.87 & 0.11 & 15.0 \\
6 & 0.99 & 0.03 & 3.5 \\
7 & 4.21 & 0.13 & 20.0 \\
8 & 3.93 & 0.10 & 4.0 \\ OEM & 24.75 & 0.00 & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Pickup route. Figure 7: The logistics operator and suppliers’ locations.