## 4 Results  
### Experimental Analysis  
The first step is to prepare all information for the model, such as zone attributes, depot attributes, vehicle attributes, and distance for every zone. In this approach, a solution is represented by a string of numbers containing a permutation of'm' depots represented by the set {1, 2, 3,..., m}, n potential customers represented by the set {m+1, m+2,..., m+n}, and N\({}_{\text{dummy}}\) Zeros to separate routes. Suppose we have two candidate depots and twelve zones in a city, as shown in Table 5. The distance for each node is calculated using the distance matrix API developed by Google based on latitude and longitude of each node. The chromosomes are the initial candidates for our proposed approach. For every chromosome, we insert a depot to serve the various zones according to the maximum \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rule No.** & **F1** & **F2** & **F3** & **F4** & **F5** & **Output** \\ \hline
1 & H & M & H & H & L & Recyclable \\
2 & L & H & M & L & H & Non-recyclable \\
3 & H & L & L & L & M & Non-recyclable \\
4 & M & L & H & M & H & Recyclable \\
5 & L & M & L & H & M & Recyclable \\
6 & H & H & L & L & M & Non-recyclable \\
7 & L & L & M & H & L & Recyclable \\... &... &... &... &... &... &... \\ N & H & M & L & H & H & Non-Recyclable \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fuzzy rule samples. waste picked up by the vehicle. A shortest distance allocation approach is implemented to determine which depot to serve the zone. A dummy zero is used in the solution to represent the separate route for each vehicle. For example, in chromosome-1, zones 5 and 14 will be served by depot 1 instead of depot 2 due to the shortest distance from the node. Thus, it is possible to minimize the total distance of each route. The solution representation for chromosome-1 is shown in Figure 6.  
The next step is the selection process. The proposed work implements tournament selection for choosing the best candidates from the current generation in GA. These selected candidates are passed on to the next generation. After choosing the individuals using tournament selection, a new generation is created by combining parent solutions to result in a new solution in GA. We apply a new type of crossover named partially matched crossover (PMX) [59; 60] to generate new offspring. This type of crossover is widely used, and the illustration is shown in Figure 7. According to Figure 7, PMX starts by selecting parents from the previous selection approach. In this example, we choose chromosome-7 as parent-1, which has the minimal fitness value. Parent-2 is generated from parent-1 by modifying the order in parent-1. The first step chooses a random segment(substrings) and copies it from parent to child. In this step, the substrings are exchanged among parents and node duplication is checked for in the strings. As there are mapping references, the duplicate nodes are replicated with another node by performing the mapping relationship. The remaining offspring can be filled similarly with the parents. The next step after crossover is the mutation operation. The mutation process is performed to increase the probability of avoiding local solutions in GA. The mutation operator alerts one or multiple genes in the children's solutions once the crossover is completed. GA improves the population using the operators until the end condition. The output of GA is used as the initial solution for the FIS approach.