## 2 State of the Art  
As early as 1991, transport agencies recognized the pavement management system as a tool for optimizing the use of the available funds [16], for its ability to choose the M&R activities, among the various possible optimal alternatives [17; 18; 19; 20]. Maintaining pavement efficiency and functionality over the years requires the conduction of M&R activities that need budgets [21] which is identifiable through optimization processes aimed at defining multi-year pavement maintenance problems. The pavement M&R optimization problem is an integer, discrete type [22], whose objectives may even contradict each other. For example, a reduction in costs could lead to a deterioration in the quality of the pavement. Several mathematical optimization methods have been used for optimal pavement management, including the optimal control theory, the linear and nonlinear programming, the dynamic programming and the integer programming [23; 24]. However, the dynamic structure of the proposed APMS problem, due to the interaction between decay functions and maintenance activities during the planning horizon, requires complex forms of mathematical formulations with consequent difficulties in solving the problem in a short time. Hence, this issue is solved by the development of artificial intelligence that allows road engineers to use non-deterministic polynomial-time (NP-hard) [25], including applications of expert systems, artificial neural networks, fuzzy logic, genetic algorithms, and hybrid systems [26]. Focusing on heuristics and meta-heuristic algorithms, since the early 1990s there has been a worldwide improvement in the use of the genetic algorithms by many civil engineering researchers [27]. These algorithms allow general-purpose stochastic optimization techniques capable of generating accurate solutions in large size networks and in reasonable time [28]. Most of the literature studies include the analysis of road infrastructure pavements, whose logic is the also the same in the airfield. In the road field, [PERSON] and [PERSON] [29] used a constraint-based genetic algorithm to minimize the total maintenance costs and maximize the performance of the road network in terms of Pavement Condition Index (PCI). [PERSON] et al. [30] first conducted an optimization methodology to maximize the pavement quality and minimize the agency costs at the pavement section level. Subsequently, they minimize agency and user costs at the network level. In this study, the pavement condition was evaluated through performance, combined and global performance indicators. [PERSON] et al. [31], used a multi-objective approach converted to a single-objective approach by dividing cost by the network's condition objective function. The pavement performance was determined by an indicator based on a trend analysis of different normalized distresses. [PERSON] et al. [32] demonstrated that non-domination sorting genetic algorithm II (NSGAII) performed better than multi-objective particle swarm optimization (MOPSO) in costs and pavement performance in terms of PCI. However, recent studies have also evaluated the performance of the pavement through other indicators, such as PSI and IRI [33; 34]. From the above it can be deduced that several variables can be considered during the optimization processes. The evaluation of the pavement condition could refer to distress type, distress amount, distress severity, smoothness, structural adequacy, and, of course, initial cost and life cycle cost [35]. Focusing on the airfield, there are few research works, and this is due to the complex managerial implications related to the multi-year pavement maintenance. For instance, [PERSON] and [PERSON] [36] applied NSGA-II for planning M&R activities in an airport pavement network based on both monetary resources and pavement conditions and assessed the pavement condition through the use of PCI indicator. [PERSON] et al.