## 6 Conclusions  
This paper illustrates the potential of using an APMS in providing flexible solutions to the needs of airport managers. For this purpose, this study investigates the impact of clustering and optimization factors in the decision-making context aimed at identifying the appropriate decision-making criteria. Using models already tested on a benchmark of randomly generated test cases, this study verifies the effectiveness of the method through the application to a real case study, to provide a better understanding of the functioning of the model and identify how much the factors influence the results obtained. Starting from the identification of the runway layout and the relative sample units and work-zones, the definition of the pavement conditions allowed testing of the model. In this framework, different strategies have been considered: (H1) during the planning period, application of a single M&R activity for each critical work-zone; (H2) merging of the activities in the critical work-zones requiring the most invasive activities at the same year; (H3) application of all the activities in all the critical work-zones in year t in which there are the greatest number of interventions; (H4) implementation of a single M&R activity or, if more convenient, of two less invasive activities for each critical work-zone; and (H5) implementation of only superficial M&R activities for each critical work-zone. The different algorithms have been tested revealing that H4 is the best heuristic method in terms of costs for solving the APMS problem for a better RL condition at minimum cost. However, the obtained numerical results revealed that no single heuristic can be selected as the most performing one. In fact, based on the Pareto-font representing the total cost in kEUR in the \(y\)-axis, the reciprocal of the residual life (1/RL) in the \(x\)-axis, it emerged that H1, H2 and H4 represent the Pareto-font of the non-dominates heuristics. Consequently, 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. Also, since H5 application leads to high costs and low RL value, implementing a temporary scenario could be considered, pending a more invasive intervention planning. Analyzing H4 and focusing on the cluster impacts on optimizing the problem, it is noted that the clustering in which the three indicators are included allows a reduction in the number of work-zones. Tending to treat more extensive areas, the clustering with the three indicators leads to a longer duration of the activities. However, it is clear that a strategy of airport managers could be to calibrate the clustering factors according to the pavement conditions. Comparing the results with and without clustering, the results reveal that the use of clustering leads to a decrease in the number of M&R activities. The use of the clustering method leads to a reduction of the deterioration rate of the structural pavement performance. Furthermore, the average trend of the performance indicators is better when using clustering. From this study, several actions can be pursued for future research works. For instance, other clustering methods could be analyzed in order to verify how much these affect the obtainable results. Referring to the optimization methods relative to the definition of multi-year pavement maintenance problems, it might be interesting to understand if the influence factors vary when additional objectives are included in the optimization. Furthermore, the results obtained from heuristics could be compared with mathematical models. In this way, the effectiveness and the reliability of the models obtained with the heuristic algorithms could be demonstrated. Conceptualization, A.D.G. and A.C.; Methodology, A.D.G., A.C. and E.R. ; Validation, A.D.G. ; Formal analysis, A.C. and E.R. ; Investigation, E.R. ; Data curation, E.R. ; Writing--original draft, E.R. ; Writing--review & editing, A.D.G. and A.C.; Supervision, A.D.G.