# A Robust Heuristics for the Online Job Shop Scheduling Problem
## 5 Experimental Results
However, our algorithm is online and, more importantly, we design it to run in real time, and thus, possibly assisting the production process. Therefore, besides the best solution obtained, it is important to have an idea what can be expected to achieve in a limited time, i.e., when only a small number of repetitions is performed. To see this, we have for each problem instance measured the percentage of times that our approach found a solution that is equal to the best known solution we got, and how many times the results are within 1%, 5%, and 10% from the best known solution. Below (see Tables 2-4), we provide the results for three JSSP instances: LA01, LA05, and FT10. It is important to note that we have similar results for all 17 JSSP instances that were included in our experiments, which are available upon request. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Number of Solutions**} & \multicolumn{6}{c}{**Percentage of Solutions**} \\ \hline \(\mathbf{T}\) & **BKS** & **Best/BKS (\%)** & **0\%** & **1\%** & **5\%** & **10\%** & **0\%** & **1\%** & **5\%** & **10\%** \\ \hline
0.10 & 666 & 100.00 & 26 & 61 & 185 & 794 & 0.13 & 0.305 & 0.925 & 3.97 \\
0.20 & 666 & 100.00 & 18 & 55 & 204 & 860 & 0.09 & 0.275 & 1.02 & 4.3 \\
0.40 & 666 & 100.00 & 20 & 59 & 214 & 862 & 0.1 & 0.295 & 1.07 & 4.31 \\
0.60 & 666 & 100.00 & 16 & 59 & 252 & 839 & 0.08 & 0.295 & 1.26 & 4.195 \\
0.80 & 666 & 100.00 & 22 & 56 & 243 & 855 & 0.11 & 0.28 & 1.215 & 4.275 \\
1.00 & 666 & 100.00 & 26 & 73 & 275 & 908 & 0.13 & 0.365 & 1.375 & 4.54 \\
2.00 & 666 & 100.00 & 15 & 52 & 202 & 714 & 0.075 & 0.26 & 1.01 & 3.57 \\
3.00 & 666 & 100.00 & 10 & 51 & 178 & 662 & 0.05 & 0.255 & 0.89 & 3.31 \\
5.00 & 666 & 100.00 & 7 & 41 & 157 & 484 & 0.035 & 0.205 & 0.785 & 2.42 \\
7.00 & 666 & 100.00 & 3 & 26 & 104 & 392 & 0.015 & 0.13 & 0.52 & 1.96 \\
10.00 & 666 & 100.00 & 2 & 13 & 74 & 288 & 0.01 & 0.065 & 0.37 & 1.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The number and percentage of solutions close to the best solution found. Varied parameter \(T\) and blocking fixed to 1%. Results for LA01. Each experiment was repeated 20,000 times. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Number of Solutions**} & \multicolumn{6}{c}{**Percentage of Solutions**} \\ \hline \(\mathbf{T}\) & **BKS** & **Best/BKS (\%)** & **0\%** & **1\%** & **5\%** & **10\%** & **0\%** & **1\%** & **5\%** & **10\%** \\ \hline
0.10 & 593 & 100.00 & 134 & 143 & 738 & 3186 & 0.67 & 0.715 & 3.69 & 15.93 \\
0.20 & 593 & 100.00 & 289 & 312 & 1605 & 4403 & 1.445 & 1.56 & 8.025 & 22.015 \\
0.40 & 593 & 100.00 & 448 & 484 & 2437 & 5679 & 2.24 & 2.42 & 12.185 & 28.395 \\
0.60 & 593 & 100.00 & 540 & 596 & 2947 & 6550 & 2.7 & 2.98 & 14.735 & 32.75 \\
0.80 & 593 & 100.00 & 551 & 607 & 3143 & 6952 & 2.755 & 3.035 & 15.715 & 34.76 \\
1.00 & 593 & 100.00 & 558 & 622 & 3378 & 7438 & 2.79 & 3.11 & 16.89 & 37.19 \\
2.00 & 593 & 100.00 & 574 & 638 & 3639 & 8459 & 2.87 & 3.19 & 18.195 & 42.295 \\
3.00 & 593 & 100.00 & 544 & 612 & 3648 & 9007 & 2.72 & 3.06 & 18.24 & 45.035 \\
5.00 & 593 & 100.00 & 476 & 515 & 3630 & 9476 & 2.38 & 2.575 & 18.15 & 47.38 \\
7.00 & 593 & 100.00 & 406 & 447 & 3447 & 9498 & 2.03 & 2.235 & 17.235 & 47.49 \\
10.00 & 593 & 100.00 & 284 & 315 & 3130 & 9637 & 1.42 & 1.575 & 15.65 & 48.185 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The number and percentage of solutions close to the best solution found. Varied parameter \(T\) and blocking fixed to 1%. Results for LA05. Each experiment was repeated 20,000 times. For example, from Table 2, we read that for several choices of parameter \(T\), around 4% of the runs provide a solution less than 10% worse than the best solution.