# Dynamic Multi-Compartment Vehicle Routing Problem for Smart Waste Collection +
## Experimental Study
### Study Results
#### 6.2.1 Experimental Results: Static M-CVRP  
Our approach in the static case was tested, considering the same data presented in Table 6, except for the amount of waste and gives in real-time. The types selected to collect with multi-compartment vehicles are Paper and/Cardboard, Plastic, and/Metal. Therefore, each vehicle needs to have two compartments, one for each type, and we consider the capacity of the two compartments given by [PERSON] et al. 2016. In addition, we add a penalty of 1 \(\upmu\) per kg for each bin that overflows. The value of the minimum fill level at which the containers must be visited when applying the proposed Metaheuristic is assumed to be equal to 70% of the container volume, i.e., each bin that has 70% or more of its capacity for both waste types.  
After applying our approach, we can see in Table 10 that the number of visited stations is lower than the number of visited bins given by Valorsul (233 against 620). Since the stations visited are the stations that have at least one container with a fill level greater than or equal to 70% of the bin capacity (having a bin capacity of 52.5 kg at most). We also note that there are plastic/metal type bins were collected even if their fill level did not reach the required weight. This is due to the ability of the plastic/metal type compartment to collect this waste type at each station visited after calculating the total disturbing quantities of this type.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Day** & **Route** & \begin{tabular}{c} **Collected** \\ **Waste (kg)** \\ \end{tabular} & \begin{tabular}{c} **Distance** \\ **(km)** \\ \end{tabular} & \begin{tabular}{c} **Visited** \\ **Bins** \\ \end{tabular} & \begin{tabular}{c} **Over-** \\ **Flowing** \\ **Bins** \\ \end{tabular} & \begin{tabular}{c} **Empty** \\ **Visited** \\ **Bins** \\ \end{tabular} & \begin{tabular}{c} **\% of** \\ **Empty** \\ **Bins** \\ \end{tabular} & \begin{tabular}{c} **Vehicles** \\ **Used** \\ **Bins** \\ \end{tabular} & \
\begin{tabular}{c} **Vehicles** \\ **Used** \\ **Bins** \\ \end{tabular} &
\begin{tabular}{c} **Vehicles** \\ **Used** \\ **Bins** \\ \end{tabular} \\ \hline
1 & 13 & 2472 & 117 & 84 & 0 & 6 & 7.1 & 1 & 61.8 \\
7 & 11 & 2343 & 129 & 74 & 0 & 17 & 23.0 & 1 & 58.6 \\
8 & 6/13 & 1420/2564 & 208/132 & 152 & 1 & 33 & 21.7 & 2 & 49.8 \\
15 & 13 & 2693 & 130 & 84 & 1 & 7 & 8.3 & 1 & 67.3 \\
19 & 11 & 2251 & 139 & 74 & 0 & 2 & 2.7 & 1 & 56.3 \\
22 & 6/13 & 1513/2195 & 221/127 & 152 & 2 & 8 & 5.3 & 2 & 46.3 \\ \hline Total & & 17450 & 1203 & 620 & 4 & 73 & 11.8 & 8 & - \\ \hline \hline \end{tabular}
\end{table}
Table 9: Proposed operations (Plastic/Metal).  
The 73 empty bins were visited during the period in the real case, while in our approach, there are no open visited stations. Moreover, no container exceeds its maximum capacity, i.e., all bins are visited before overflowing, while there are four overflowing bins for actual Valorsul operations.  
Regarding the distances, a reduction of 55.77% of the traveled distance (532 km vs. 1203 km), so we see that the total distance to be crossed in the actual case of Valorsul is twice as large. This is justified by the fact that the vehicles travel long distances to collect fewer bins, which requires a model capable of choosing the containers worth visiting according to their fill levels and location.  
When the hybrid metaheuristic is applied, the vehicle usage rates are always higher than 50%, varying between 53% (day 22) and 82% (day 15). Valorsul vehicle usage rates are lower, starting at 46% (day 22) and reaching 67% (day 15) at maximum, so this allows us to say that our approach gives satisfactory results.