# Solving the Parallel Drone Scheduling Traveling Salesman Problem via Constraint Programming
## 4 Computational Experiments
### Main Experimental Results
#### 4.2.3 Instances Proposed in [13]
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Instance** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **Lei and Chen** & **[PERSON] et al.** & **[PERSON] et al.** & **CP** & \\  & [7]–UB & [10]–UB & [11]–UB & [12]–UB & [14]–UB & [13]–UB & **Opt Cost** & **Sec Found** & **Sec Opt** \\ \hline berline2,80,80.1,2.1 & 6386.5 & 6386.5 & 6386.5 & 6386.5 & - & 6386.5 & 3.5 & 4.5 \\ berline2,80,80.1,2.1 & 7802.0 & 780.0 & 7830.0 & 7830.0 & - & - & 7830.0 & 0.7 & 1.0 \\ berline2,80.0,1.2,1 & 9490.0 & 9350.0 & 9450.0 & 9450.0 & 9450.0 & - & 9495.0 & 0.4 & 0.5 \\ berline2,80.0,1.2,1 & 9350.0 & 9350.0 & 9350.0 & - & - & 9350.0 & 1.1 & 1.1 \\ berline2,80.0,1.2,1 & 8300.0 & 8300.0 & 8300.0 & 8300.0 & - & - & 8300.0 & 1.0 & 1.1 \\ berline2,80.0,1.2,1 & 7410.0 & 7410.0 & 7410.0 & - & - & 7410.0 & 3.0 & 6.7 \\ berline2,80.0,1.1,1 & 7460.0 & 6492.0 & 6492.0 & 6492.0 & - & - & 6192.0 & 13.7 & 19.6 \\ berline2,80.0,1.1,1 & 7460.0 & 7480.0 & 7450.0 & 7450.0 & - & 7450.0 & 1.5 & 5.4 \\ berline2,80.0,1.3,1.3 & 5606.6 & 5666.6 & 5666.6 & - & - & 5656.6 & 1.9 & 2.4 \\ berline2,80.0,1.4,1.4 & 5802.0 & 520.0 & 5209.0 & 5209.0 & - & - & 5209.7 & 1.0 & 1.8 \\ berline2,80.0,1.5,1.5 & 5190.0 & 5190.0 & 5190.0 & 5190.0 & - & - & 5190.0 & 2.0 & 2.1 \\ berline2,80.0,2.2,1 & 5398.8 & 5209.0 & 5502.0 & 5209.0 & - & 5209.7 & 5209.7 & 2.8 & 5.3 \\ berline2,80.3,2.3 & 5190.0 & 5190.0 & 5190.0 & 5190.0 & - & 5190.0 & 2.2 & 2.2 \\ berline2,80.0,4.2,3 & 5190.0 & 5190.0 & 5190.0 & 5190.0 & - & 5190.0 & 2.2 & 2.2 \\ berline2,80.0,8.2.3,1 & 5190.0 & 5190.0 & 5190.0 & 5190.0 & - & 5190.0 & 2.2 & 2.2 \\ berline2,80.0,5.2,3.1 & 5190.0 & 5190.0 & 5190.0 & 5190.0 & - & 5190.0 & 2.2 & 2.2 \\ berline2,80.0,8.5.2,3.1 & 5190.0 & 5190.0 & 5190.0 & - & - & 5190.0 & 1.2 & 1.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Instances originated from _bjen12_ in [7]. Optimal solution costs. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Instance** & **Mibado Sales et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **Lei and Chen** & **[PERSON] et al.** & **[PERSON] et al.** & **CP** & **CCP** & **CCP** \\  & (P1)**-**UB** & [10]**-**UB** & [11]**-**UB** & [12]**-**UB** & [14]**-**UB** & [13]**-**UB** & [13]**-**UB** & **Opt Cost** & **Sec Found** & **Sec Opt** \\ \hline eill01,0 \(\beta\)0.80,1.2,1 & 564.0 & 564.0 & 564.0 & 564.0 & - & 564.0 & - & 564.0 & 34.3 & 39.5 \\ eill01,10.1,2.1 & 650.0 & 669.0 & 649.0 & 649.0 & 649.0 & - & 649.0 & 71.4 & 80.8 \\ eill01,0.10,2.1,2 & 819.0 & 819.0 & 819.0 & 819.0 & 819.0 & 819.0 & - & 819.0 & 5.9 & 6.0 \\ eill01,01.0,2.1,2.1 & 738.0 & 736.0 & 726.0 & 726.0 & 726.0 & 726.0 & - & 726.0 & 15.7 & 20.2 \\ eill01,01.0,2.1,2.1 & 646.0 & 646.0 & 646.0 & 646.0 & 646.0 & - & 646.0 & 21.4 & 38.8 \\ eill01,01.0,2.1,2.1 & 578.0 & 578.0 & 578.0 & 578.0 & 578.0 & - & 578.0 & 31.4 & 35.9 \\ eill01,01.0,1.2,1.1 & 554.4 & 560.0 & 560.0 & 560.0 & 560.0 & - & 560.0 & 54.8 & 92.4 \\ eill01,01.0,3.0,1.1,1 & 560.0 & 660.0 & 660.0 & 660.0 & 650.0 & - & 560.0 & 70.3 & 92.5 \\ eill01,01.0,1.3,1.1 & 594.0 & 530.0 & 530.0 & 502.0 & 503.2 & - & 542.2 & 22.2 & 44.2 \\ eill01,01.0,1.4,1 & 456.0 & 454.0 & 456.0 & 456.0 & 456.0 & - & 456.0 & 16.0 & 36.0 \\ eill01,01.0,1.5,1.1 & 420.8 & 427.0 & 420.8 & 420.8 & 420.8 & 420.8 & 420.8 & 27.6 & 35.5 \\ eill01,01.0,2.1,2.1 & 465.0 & 456.0 & 456.0 & 456.0 & 458.0 & 456.0 & 58.7 & 276.0 \\ eill01,01.0,3.2,1 & 395.0 & 395.0 & 395.0 & 395.0 & - & 395.0 & - & 395.0 & 29.9 & 65.9 \\ eill01,01.0,3.2,1 & 367.7 & 366.0 & 346.0 & 346.0 & 346.0 & _354.0_ & 34.0 & 35.5 & 36.7 \\ eill01,01.0,3.5,2.1 & 319.7 & 318.0 & 318.0 & 318.0 & 318.0 & - & 318.0 & 11.9 & 33.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Instances originated from _eill01_ in [\(\Gamma\)]. Optimal solution costs.