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In the airport pavement management systems (APMSs), a focus point is the decision-making process. It enables finding the optimal strategy for maintaining a flight infrastructure in adequate condition over a given period, while considering the operating conditions of the airside. In this context, the present study analyzes the factors involved in the optimization processes by investigating how much they influence the solutions. Using the analysis processes connected to the APMS, the present study also includes the identification of specific intervention areas through clustering algorithms, minimizing the fixed operating costs. More specifically, the use of K-means clustering and the heuristic algorithms connected to the choices of the maintenance activities, allow possible scenarios replicating the different needs of managers to be investigated. In this way, the research work analyzes the influence of the alternatives in terms of pavement quality and total activities duration. Through this study it is shown that there is not a unique optimal strategy, but several possible solutions that can be undertaken by the airport managers according to their needs. However, the comparison of the results obtained in this study could become a useful tool for airport managers for better planning and management of the flight infrastructures.
Graziano et al. (Thu,) studied this question.