# No-Wait Job Shop Scheduling Using a Population-Based Iterated Greedy Algorithm
## 4 Computational Results and Comparisons
### Comparisons with Other Metaheuristics
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Instance.** & \(n\)**, \(m\)** & **BKS** & \multicolumn{2}{c}{**MCLM**} & \multicolumn{2}{c}{**HABC**} & \multicolumn{2}{c}{**PBIG**} \\ \hline  & & & **RPD\({}^{B}\)** & **ARPD** & \(\mathbf{T^{A}(s)}\) & **RPD\({}^{B}\)** & **ARPD** & \(\mathbf{T^{A}(s)}\) & **RPD\({}^{B}\)** & **ARPD** & \(\mathbf{T^{A}(s)}\) \\ \hline Ft06 & 6, 6 & 73 & **0.00** & **0.00** & 0.00 & **0.00** & **0.00** & 0.15 & **0.00** & **0.00** & 0.65 \\ La01 & 10, 5 & 971 & **0.00** & **0.00** & 4.55 & 0.41 & 0.41 & 0.78 & **0.00** & **0.02** & 1.50 \\ La02 & 10, 5 & 937 & **0.00** & **0.00** & 7.60 & 2.56 & 2.56 & 1.41 & **0.00** & **0.00** & 1.50 \\ La03 & 10, 5 & 820 & **0.00** & **0.00** & 3.10 & **0.00** & **0.00** & 0.70 & **0.00** & **0.00** & 1.50 \\ La04 & 10, 5 & 887 & **0.00** & **0.00** & 6.25 & **0.00** & **0.00** & 0.87 & **0.00** & **0.00** & 1.50 \\ La05 & 10, 5 & 777 & 0.51 & 0.90 & 3.90 & 0.51 & 0.51 & 1.14 & **0.00** & **0.00** & 1.50 \\ Ft10 & 10, 10 & 1607 & **0.00** & **0.00** & 7.85 & **0.00** & **0.00** & 9.86 & **0.00** & **0.00** & 3.00 \\ Orb01 & 10, 10 & 1615 & **0.00** & **0.00** & 6.65 & **0.00** & **0.00** & 8.00 & **0.00** & **0.00** & 3.00 \\ Orb02 & 10, 10 & 1485 & 2.16 & 2.16 & 6.70 & **0.00** & **0.00** & 6.86 & **0.00** & **0.00** & 3.00 \\ Orb03 & 10, 10 & 1599 & **0.00** & **0.00** & 13.75 & **0.00** & **0.00** & 10.07 & **0.00** & **0.00** & 3.00 \\ Orb04 & 10, 10 & 1653 & **0.00** & 0.12 & 7.85 & **0.00** & **0.00** & 5.28 & **0.00** & **0.00** & 3.00 \\ Orb05 & 10, 10 & 1365 & **0.00** & **0.00** & 8.50 & 0.37 & 0.37 & 4.77 & 0.15 & 0.15 & 3.00 \\ Orb06 & 10, 10 & 1555 & **0.00** & **0.00** & 3.55 & **0.00** & **0.00** & 5.76 & **0.00** & **0.00** & 3.00 \\ Orb07 & 10, 10 & 689 & **0.00** & **0.00** & 7.25 & NA & NA & NA & **0.00** & **0.00** & 3.00 \\ Orb08 & 10, 10 & 1319 & **0.00** & **0.00** & 6.40 & **0.00** & **0.00** & 8.99 & **0.00** & **0.00** & 3.00 \\ Orb09 & 10, 10 & 1445 & **0.00** & **0.00** & 4.25 & **0.00** & 0.28 & 4.10 & **0.00** & **0.00** & 3.00 \\ Orb10 & 10, 10 & 1557 & **0.00** & **0.00** & 11.85 & **0.00** & **0.00** & 5.51 & **0.00** & **0.00** & 3.00 \\ La16 & 10, 10 & 1575 & 1.84 & 1.84 & 5.65 & **0.00** & **0.00** & 5.43 & **0.00** & **0.00** & 3.00 \\ La17 & 10, 10 & 1371 & **0.00** & 0.12 & 11.85 & **0.00** & **0.00** & 4.62 & **0.00** & **0.00** & 3.00 \\ La18 & 10, 10 & 1417 & 2.82 & 2.82 & 6.15 & **0.00** & 5.22 & 7.13 & **0.00** & **0.00** & 3.00 \\ La19 & 10, 10 & 1482 & **0.00** & 0.61 & 5.00 & **0.00** & 0.43 & 3.44 & **0.00** & **0.00** & 3.00 \\ La20 & 10, 10 & 1526 & 1.31 & 1.31 & 5.40 & **0.00** & **0.00** & 3.00 & **0.00** & **0.00** & 3.00 \\ Average & & & 0.39 & 0.45 & 6.55 & 0.23 & 0.47 & 4.63 & **0.01** & **0.01** & 2.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results for the modified complete local search with memory (MCLM), hybrid artificial bee colony (HABC) and population-based iterated greedy (PBIG) on the small instances. Table 3 shows that the PBIG can optimally solve all the small instances except Orb05. Further, for all the instances except Orb05 and La01, the PBIG can find the optimal solutions for every single run since the RPD\({}^{\text{B}}\) and ARPD values are both 0.00. The average RPD\({}^{\text{B}}\) and ARPD values of the PBIG are both 0.01, which are better than those of the MCLM (0.39, 0.45) and HABC (0.23, 0.47), whereas the average computational time of the PBIG (2.55 s) is much less than that of the MCLM (6.55 s) and HABC (4.63 s). It can be seen clearly from Table 4 that the results obtained by the PBIG and HABC are clearly better that that of the MCLM. Besides, Table 4 illustrates that the PBIG is a competitive algorithm for the large instances, and the average computational time of the PBIG is less than half of that of the HABC. The results also show that the PBIG finds equal best solution for 25 instances and better best solution for 9 instances when compared with the HABC.