## 5 Discussion  
Using the methodology and assumptions illustrated above, the present Section aims to demonstrate the potential of using an APMS in providing flexible solutions to the needs of airport managers. Referring to the parameters and assumptions of Section 3, the study aims to analyze the results obtained by applying the five heuristic algorithms to the case study of the Catania airport in terms of pavement quality and total duration of the activities at the end of the planning period T. In addition, the impact of the clustering model on the solutions obtained is verified. The present study considers that in Catania airport the presence of a single runway determines the total absence of operation in the case of maintenance activities on it. For this reason, surface requalification maintenance activities often take place within a few hours, at night, reducing the inconvenience to a minimum. Hence the results obtained from this work, with maintenance activities in consecutive years. In any case, the threshold values defined in the airport context are clearly defined to guarantee constant safety of \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**M\&R Activities**} & \multicolumn{4}{c}{**Effects**} \\ \cline{2-5}  & \(\mathbf{[\ell/m^{2}]}\) & **ARL [Year]** & **IRI [m/km]** & **PCI** \\  & & \(Th_{RL}\) = 0-Year & \(Th_{IRI}\) = 3.60 m/km & \(Th_{PCI}\) = 25 \\ \hline
0. **Do-nothing (\(x_{uvr}\) = 0)** & – & \multicolumn{2}{c}{Do nothing} \\ \hline
**1. Deep structural (\(x_{uvr}\) = 1)** & 168.89 & 20 & 0.7 & 95 \\ \hline
2. **Intermediate structural (\(x_{uvr}\) = 2)** & 102.04 & 5 & 0.7 & 95 \\ \hline
3. **Surface structural (\(x_{uvr}\) = 3)** & 46.94 & 2 & 0.7 & 95 \\ \hline
4. **Functional (\(x_{uvr}\) = 4)** & 18.05 & 0 & 0.7 & 95 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Costs and effects of the M&R activities. Figure 2: M&R activities. flight operations without any derogation. The strategy adopted is to always carry out the work before the total breakdown of the infrastructure in question. Figure 3 shows a Pareto diagram in which the \(y\)-axis represents the total cost in kEUR, and the \(x\)-axis is the reciprocal of the residual life (1/RL) at the end of the planning time (T = 6 years). In this way, it is possible to verify the trend of the structural pavement conditions over the years for the different heuristics. This study confirms that H1, H2 and H4 represent the Pareto-font of the non-dominates heuristics. H4 is the best heuristic method in terms of costs for solving the APMS problem for a better RL condition at minimum cost. However, this study confirms that there is not a unique optimal strategy, but several possible solutions that can be undertaken by the airport managers according to their needs. From Figure 3 it has emerged that H5 could be considered an inconvenient strategy as it is associated with high costs and low RL value. Focusing on the cluster impacts on optimizing the problem, the subsequent analyses are based on H4. In fact, the authors have verified, except for H5, H4 is the heuristic that tends to have a shorter total duration of interventions than the other algorithms. Since H5 is built considering only functional activities not always resolutive, H4 is therefore the optimal strategy for solving pavement problems in the shortest possible time. In fact, H4 tends to choose less invasive maintenance treatments that allows for reduced impact on airport operations. In this regard, Figure 4 illustrates the total duration of the M&R activities in the \(y\)-axis, and the number of work-zones in the \(x\)-axis. First of all, it is noted that the clustering in which the three indicators are included allows a reduction in the number of work-zones. The grouping of the areas into work-zones is conducted using a K-means method based on three well-known performance indicators, which in turns considers adjacency among the pavement portions as a further clustering constraint [15].