# Heuristic Method for Decision-Making in Common Scheduling Problems
## 7 Experimental Results
This is confirmed by the percentage error value (the largest difference in favor of the presented algorithms was 13.818% for the _GR-3_ network). It can be concluded that for regular networks the algorithm that is the combination of the selection criteria \(q3\) and node \(\widehat{s1}\) gives the best results. However, the same trend does not apply to irregular networks. The tests were performed under uniform simulated conditions, and the algorithms did not resolve all networks (_Alg._\(q1-\widehat{s1}\), \(q1-\widehat{s2}\), \(q1-\widehat{s4}\), and \(q1-\widehat{s5}\) for the _GR-1b_ network, and _Alg._\(q2-\widehat{s1}\) for the _Glr-5_ network). At the same time, there are no solutions for an algorithm that generates single whole trajectories. On the other hand, it is important to note that in some cases algorithms generating final part of trajectories have found a solution (_Alg._\(q1\) for _GR-1b_ and _Alg._\(q2\) for _GR-3_) with suboptimal cost while the algorithm generated independently of whole trajectories did not find any admissible solutions. Table 13 presents a summary of the obtained results: average total cost, average percentage, and average simulation time. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Problem Example** & **Obtained Total Cost** & **Percentage Error** & **Simulation Time (ms)** \\ \hline _Alg._\(q1\) & 17,284.10 & 4.597\% & 265 \\ _Alg._\(q1-\widehat{s1}\) & 17,284.10 & 4.597\% & 343 \\ _Alg._\(q1-\widehat{s2}\) & 17,284.10 & 4.597\% & 218 \\ _Alg._\(q1-\widehat{s3}\) & 17,139.70 & 3.724\% & 859 \\ _Alg._\(q1-\widehat{s4}\) & 17,202.00 & 4.101\% & 375 \\ _Alg._\(q1-\widehat{s5}\) & 17,023.40 & 3.020\% & 421 \\ \hline _Alg._\(q2\) & 16,792.20 & 1.621\% & 171 \\ _Alg._\(q2-\widehat{s1}\) & 16,780.80 & 1.552\% & 187 \\ _Alg._\(q2-\widehat{s2}\) & 16,780.80 & 1.552\% & 156 \\ _Alg._\(q2-\widehat{s3}\) & 16,792.20 & 1.621\% & 93 \\ _Alg._\(q2-\widehat{s4}\) & 16,766.00 & 1.462\% & 125 \\ _Alg._\(q2-\widehat{s5}\) & 16,766.00 & 1.462\% & 171 \\ \hline _Alg._\(q3\) & 16,748.20 & 1.354\% & 156 \\ _Alg._\(q3-\widehat{s1}\) & 16,653.70 & 0.782\% & 187 \\ _Alg._\(q3-\widehat{s2}\) & 16,748.20 & 1.354\% & 156 \\ _Alg._\(q3-\widehat{s3}\) & 16,748.20 & 1.354\% & 187 \\ _Alg._\(q3-\widehat{s4}\) & 16,748.20 & 1.354\% & 140 \\ _Alg._\(q3-\widehat{s5}\) & 16,748.20 & 1.354\% & 156 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Obtained results for regular network _GR-2_ (Optimal cost 16,524.40). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Problem Example** & **Obtained Total Cost** & **Percentage Error** & **Simulation Time (ms)** \\ \hline _Alg._\(q1\) & - & - & - \\ _Alg._\(q1-\widehat{s1}\) & - & - & - \\ _Alg._\(q1-\widehat{s2}\) & - & - & - \\ _Alg._\(q1-\widehat{s3}\) & 17,008.60 & 2.844\% & 984 \\ _Alg._\(q1-\widehat{s4}\) & - & - & - \\ _Alg._\( \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Problem Example** & **Obtained Total Cost** & **Percentage Error** & **Simulation Time (ms)** \\ \hline _Alg. q2_ & - & - & - \\ _Alg. q2_\(-\widehat{s}1\) & 15,147.50 & \(-13.189\%\) & 187 \\ _Alg. q2_\(-\widehat{s}2\) & 15,147.50 & \(-13.189\%\) & 140 \\ _Alg. q2_\(-\widehat{s}3\) & 15,409.30 & \(-11.689\%\) & 187 \\ _Alg. q2_\(-\widehat{s}4\) & 15,409.80 & \(-11.689\%\) & 125 \\ _Alg. q2_\(-\widehat{s}5\) & 15,147.50 & \(-13.189\%\) & 171 \\ \hline _Alg. q3_ & 16,282.10 & \(-6.687\%\) & 171 \\ _Alg. q3_\(-\widehat{s}1\) & 15,037.80 & \(-13.818\%\) & 140 \\ _Alg. q3_\(-\widehat{s}2\) & 15,251.90 & \(-12.591\%\) & 156 \\ _Alg. q3_\(-\widehat{s}3\) & 15,660.70 & \(-10.248\%\) & 171 \\ _Alg. q3_\(-\widehat{s}4\) & 15,514.20 & \(-11.088\%\) & 125 \\ _Alg. q3_\(-\widehat{s}5\) & 15,251.90 & \(-12.591\%\) & 187 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._