# Dynamic Multi-Compartment Vehicle Routing Problem for Smart Waste Collection +
## Experimental Study
### Study Results
#### 6.2.2 Experimental Results: Dynamic M-CVRP  
In this part, we present the results of our proposed approach for the resolution of the dynamic M-CVRP, where we must satisfy the integration of new stations when the alarms are triggered for at least one waste type, then the routes have been planned. Their exploitation started by adding the characteristics below to obtain the dynamic problem:  
The discretization of a working day. The time of appearance of a station. Our tests were carried out over six days, and we kept the parameter values used in the static case, and we simulated the length of a day by 22,000 s (\(t_{l}\) = 22,000 s), and thus the algorithm is stopped if the elapsed time is greater than or equal to \(t_{l}\). This duration is divided into time slices having variable sizes according to the course of an iteration of our approach \(T_{iter}\). This group contains six days with 5 degrees of dynamism (dod) ([PERSON] et al. [16]) which are 90%, 10%, 50%, 70%, and 30% (dod = 0% present the static case). Table 11 reports the obtained results on the different degrees of dynamism for our modified genetic algorithm. It indicates the total traveled distance and the number of stations visited. We note, on the one hand, that respecting all the constraints, the dynamic aspect influences the number of stations visited and the distances covered, it is easy to see, on the one hand, that if we increase the degree of dynamism, the total distance increases, then it decreases when the degree reaches 50%, then it goes up or remains unstable, and on the other hand, we notice that in some cases, even if the distance increases, the number of stations visited remains the same, or else diminished, This is due to the fact that the number of visited stations does not depend only on the distance traveled but specifically \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Day/Route**} & **Collected** & **Visited** & **Visited** & **Distance** & **Level of a** & **Empty** & **Vehicles** & **Compartant** & **Vehicle** \\  & **Waste (kg)** & **Stations** & **PC** & **PM** & **(km)** & **Onerflow** & **Visited** & **Used** & **Rate \%** & **Usage** \\  & **PC** & **PM** & **Stations** & **PC** & **PM** & **Overflow** & **Stations** & & **PC** & **PM** & **Rate \%** \\ \hline
1/13 & 1940 & 35 & 35 & 33 & 76.41 & 0 & 0 & 1 & 77.6 & 75.47 & 76.54 \\
7/11 & 1820 & 33 & 33 & 33 & 78.32 & 0 & 0 & 1 & 72.8 & 67.1 \\  & 921 & 2890 & & & & & & & 61.4 & \\
8/6 \& 8/13 & 1718 & 51 & 51 & 46 & 119.10 & 0 & 0 & 2 & 57.8 & 57.53 \\
15/13 & 2113 & & & & & & & & 84.52 & & 82.43 \\
1205 & 1205 & 39 & 39 & 39 & 87.95 & 0 & 0 & 1 & 60.68 & 58.38 \\
19/11 & 1517 & & & & & & & & & 56.07 & 58.38 \\
22/6 \& & & & & & & & & & 56.38 & \\
22/13 & 1546 & 49 & 49 & 47 & 116.21 & 0 & 0 & 2 & 51.53 & 53.96 \\ Total & 13,099 & & & & & & & & & \\
7363 & 233 & 233 & 206 & 532.04 & 0 & 0 & 8 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Solutions obtained through the hybrid Genetic Algorithm. on the number of stations triggering the alarm, and which are integrated into the different time slices. And in DVRP, the vehicle can service stations on its way, which can reduce the distances covered. In static VRP, the more \(N_{l_{l}}\) contains more stations, the simulation results tend to be much better because the vehicle often returns to the depot with approximately full loading, and thus, the vehicle number is reduced. However, in DVRP, it is uncertain that the optimization result is better when \(N_{l_{l}}\) contains more stations because there are undefined factors. Generally, at the end of each day, the number of unvisited stations with at least one waste type with a filling rate greater than or equal to 70% is negligible compared to the number of stations having triggered the alarm and which is visited regardless of the dynamism degree. This makes it possible to say that our approach gives satisfactory results in terms of quality of service in the dynamic case.