## 3 Proposed Methodology
### Decoding the Customer Vector
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Problems** & \(BKS\) & \(Cost_{BLS-PSO}\) & _gap_ & \(AvgT\) & \(k_{2}\) & \(k_{3}\) \\ \hline A-n32-k5 & 784 & 784 & 0.00 & 0.15 & 0.1 & 0.1 \\ A-n33-k5 & 661 & 661 & 0.00 & 0.15 & 0.4 & 0.5 \\ A-n33-k6 & 742 & 742 & 0.00 & 0.43 & 0.4 & 0.0 \\ A-n34-k5 & 778 & 778 & 0.00 & 0.8 & 0.5 & 0.8 \\ A-n36-k5 & 799 & 799 & 0.00 & 0.76 & 0.0 & 0.8 \\ A-n37-k5 & 669 & 669 & 0.00 & 0.92 & 0.8 & 1.0 \\ A-n37-k6 & 949 & 949 & 0.00 & 0.64 & 0.5 & 0.2 \\ A-n38-k5 & 730 & 730 & 0.00 & 0.69 & 1.0 & 0.8 \\ A-n39-k5 & 822 & 822 & 0.00 & 1.07 & 0.5 & 0.3 \\ A-n39-k6 & 831 & 831 & 0.00 & 1.15 & 0.4 & 0.7 \\ A-n44-k6 & 937 & 937 & 0.00 & 1.03 & 0.3 & 0.0 \\ A-n45-k6 & 944 & 944 & 0.00 & 1.76 & 0.0 & 0.2 \\ A-n45-k7 & 1146 & 1146 & 0.00 & 1.31 & 0.5 & 0.0 \\ A-n46-k7 & 914 & 914 & 0.00 & 1.29 & 0.6 & 0.3 \\ A-n48-k7 & 1073 & 1073 & 0.00 & 1.45 & 0.9 & 0.1 \\ A-n53-k7 & 1010 & 1010 & 0.00 & 2.54 & 0.9 & 1.0 \\ A-n54-k7 & 1167 & 1167 & 0.00 & 7.61 & 0.2 & 0.5 \\ A-n55-k9 & 1073 & 1073 & 0.00 & 8.47 & 0.5 & 0.4 \\ A-n60-k9 & 1354 & 1354 & 0.00 & 8.78 & 0.6 & 0.2 \\ A-n61-k9 & 1034 & 1034 & 0.00 & 7.98 & 0.7 & 1.0 \\ A-n62-k8 & 1288 & 1296 & 0.62 & 9.53 & 0.7 & 0.4 \\ A-n63-k9 & 1616 & 1616 & 0.00 & 9.22 & 0.9 & 0.3 \\ A-n63-k10 & 1314 & 1314 & 0.00 & 10.14 & 0.8 & 1.0 \\ A-n64-k9 & 1401 & 1415 & 1.00 & 18.86 & 0.5 & 0.6 \\ A-n65-k9 & 1174 & 1174 & 0.00 & 16.5 & 0.2 & 0.8 \\ A-n69-k9 & 1159 & 1159 & 0.00 & 18.41 & 0.6 & 0.6 \\ A-n80-k10 & 1763 & 1766 & 0.17 & 16.75 & 0.2 & 1.0 \\ \hline \multicolumn{5}{c}{Average} & 0.07 & 5.5 & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of BLS-PSO in solving the benchmark problems in Set A. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Problems** & \(BKS\) & \(Cost_{BLS-PSO}\) & _gap_ & \(AvgT\) & \(k_{2}\) & \(k_{3}\) \\ \hline B-n31-k5 & 672 & 672 & 0.00 & 2.07 & 0.5 & 0.5 \\ B-n34-k5 & 788 & 788 & 0.00 & 2.42 & 0.1 & 0.9 \\ B-n35-k5 & 955 & 955 & 0.00 & 1.12 & 0.6 & 0.8 \\ B-n38-k6 & 805 & 805 & 0.00 & 3.01 & 0.8 & 0.9 \\ B-n39-k5 & 549 & 549 & 0.00 & 3.59 & 0.0 & 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of BLS-PSO in solving the benchmark problems in Set B. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Problems** & _BKS_ & _Cost\({}_{\text{BLS-PSO}}\)_ & _gap_ & _AvgT_ & \(\mathbf{k_{2}}\) & \(\mathbf{k_{3}}\) \\ \hline E-n22-k4 & 375 & 375 & 0.00 & 0.21 & 0.3 & 0.1 \\ E-n23-k3 & 569 & 569 & 0.00 & 0.2 & 0.1 & 0.6 \\ E-n30-k3 & 534 & 534 & 0.00 & 0.3 & 0.2 & 0.2 \\ E-n33-k4 & 835 & 835 & 0.00 & 1.77 & 0.4 & 0.3 \\ E-n51-k5 & 521 & 521 & 0.00 & 2.81 & 0.1 & 0.8 \\ E-n76-k7 & 682 & 687 & 0.73 & 13.55 & 0.2 & 0.9 \\ E-n76-k8 & 735 & 735 & 0.00 & 27.36 & 0.2 & 0.8 \\ E-n76-k10 & 830 & 830 & 0.00 & 18.62 & 0.4 & 0.6 \\ E-n76-k14 & 1021 & 1021 & 0.00 & 14.69 & 0.2 & 0.3 \\ E-n101-k8 & 815 & 815 & 0.00 & 21.27 & 0.9 & 0.9 \\ E-n101-k14 & 1067 & 1095 & 2.62 & 25.81 & 0.2 & 0.2 \\ \hline \multicolumn{2}{c}{Average} & & 0.31 & 11.51 & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of BLS-PSO in solving the benchmark problems in Set E. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Problems** & _BKS_ & _Cost\({}_{\text{BLS-PSO}}\)_ & _gap_ & _AvgT_ & \(\mathbf{k_{2}}\) & \(\mathbf{k_{3}}\) \\ \hline F-n45-k4 & 724 & 724 & 0.00 & 9.2 & 0.7 & 0.7 \\ F-n72-k4 & 237 & 237 & 0.00 & 7.26 & 0.9 & 0.2 \\ F-n135-k7 & 1162 & 1171 & 0.78 & 60.32 & 0.4 & 0.7 \\ \hline \multicolumn{2}{c}{Average} & & 0.26 & 25.59 & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of BLS-PSO in solving the benchmark problems in Set F.