*4.1.1 Comparison between original situation and optimized scenario (Mondays)*

The final solution obtained for Mondays is the one shown in **Table 2**. As a result, we obtain the following if we consider the same number of days covered per sector (if we consider days: 16/08, 9/08, and 27/07):


The difference in distance is because of the new constraints that are not practiced by the concessionaire are here considered in the final planned routes. The reduction is in fact because of the better-balanced routes take less time spent for collection in 6.15% as the plan established.

#### *4.1.2 Comparison between original situation and optimized scenario (Tue-sat)*

In the computation of the sectors and routes for daily night collection in downtown Petropolis/RJ, we needed to reconsider the previous collection sectors and the production statistics for Tuesdays-Saturdays. There were plenty of


#### **Table 2.**

*Solution obtained after all corrections in the field, to be applied to the sectors on Mondays at downtown Petropolis/RJ.*

*Optimized Planning and Management of Domiciliary and Selective Solid Waste... DOI: http://dx.doi.org/10.5772/intechopen.111432*

information for these cases, and we could build the routes with much greater success than before.

With the considerable decrease in the daily production between Mondays and the rest of the week as shown in **Figure 7**, the sectors were recalculated with the adequate parameters of production. We found a new configuration with four sectors. Running up the capacitated clustering according to the vehicle's technical capacity in the sectors to build the circuits, meaning each trip, as can be seen in **Figure 1c** and **d**, the solution achieved impressive savings in the first plan produced. Considering that many local constraints were not satisfied, we included them in the graph and obtained a new plan for the first execution in the field. As mentioned previously, the process continued until both constraint satisfaction and app return were in order; we tried four times, until the last, although still with problems with vehicle's breakdown, it meant that the completion was not compromised at all. **Table 3** shows the last result obtained, **Table 4** presents the plan produced by SisRot® LIX, and in **Table 5**, we present the estimated savings considering the numbers from production spreadsheets (92 trips, 27/07/21–29/01/22) with vehicles' breakdown during service and without this occurrence.

The savings achieved 17,86% with distance. The final plan also reduced 1 vehicle and 1 driver in 5 for both resources. We considered the effect of unemployment, and we maintained the same number of collectors, but it caused prejudice to the process, because the collection with three men was faster (9.54%) than a collection with four men. It means that on average a team with three men collects 2548.38 kg/h, while a team with four men collects 2326.36 kg/h in Petropolis/RJ. In **Table 6**, we can see the average efficiency (kg/man-h) of the collectors between sectors and days of the week to better understand this important variable in decision-making.

The experience with CherryTrack® LIX was fundamental in the process of establishing the sectors and routes in the field. After training drivers for a week, we had big problems with those who did not repass the training of studying the app at home. The only one who did it was successful in every route he did and always finalized his job before all. He was used as an example for the others, and three of them succeeded in the end. One more resistant did not want to use the technology and always avoided it. In the end, the supervisor and all drivers were fully integrated with the app and did not want to look at maps anymore. The possibility to control all the drivers anywhere, even driving a truck, was considered the most advantageous part of the app for the supervisor, and for the drivers, it was the route indication while in operation and the state of completeness of the service any time.


**Table 3.**

*Result of the application in the field, for the 4th revision plan to Tuesday–Saturday.*


 **4.** *The result of the 4th revision plan to Tuesday–Saturday (the time to complete the task is overestimated).*

**Table**

*Optimized Planning and Management of Domiciliary and Selective Solid Waste... DOI: http://dx.doi.org/10.5772/intechopen.111432*


#### **Table 5.**

*Result in savings considering service with and without vehicle breakdown.*


#### **Table 6.**

*Speeds of operation considered in the project for the city.*

We applied the same SARP methodology explained above in domiciliary waste collection to other Brazilian cities like Franca/SP, Campo Grande/MS, Mazagão/AP, and Búzios/RJ with success, obtaining 12–28% savings in the total distance traveled and from 5 to 9% savings in the staff's working hours. The relevance of the methodology opened new opportunities to make the waste collection more predictable and less susceptible to the variations of the load per day.
