# Payment Maintenance Decision Making Based on Optimization Models
## 1 Introduction  
Continuous urbanization in China has significantly increased the cumulative mileage of roads, which are considered the arteries of economic and social activities at the national and local levels. As an essential component of road infrastructure, a pavement is a type of hard surface made from durable surface material, which is able to withstand traffic and harmful environments. Because of increasing traffic volumes with heavy loads and the impacts of adverse environments, regular pavement maintenance is necessary to repair damage and mitigate degradation. A sufficient budget should be allocated to maintain the pavement at an appropriate condition, but insufficient budget is the primary obstacle of pavement maintenance [1]. When conducting large-scale pavement maintenance, it is important to consider the maintenance costs while ensuring that maintenance quality meets the requisite standards. The selection of a proper pavement maintenance scheme is a multiobjective optimization task. Therefore, determining the optimal maintenance scheme is crucial for decision making in pavement maintenance [2, 3]. Decision making in pavement maintenance is a complicated, nondeterministic polynomial time (NP)-hard problem [4]. Maintenance personnel generally make judgments based on their personal experience when facing pavement maintenance tasks. Typical considerations include determinationof which roads need maintenance, measures to be adopted for pavement maintenance, estimation of the maintenance cost, and the deadline for completing the maintenance. However, decision making based on human experience cannot control pavement maintenance costs or evaluate whether the maintenance quality meets the relevant standards. This can subsequently lead to deterioration of road structures or pose challenges in the assessment of maintenance outcomes. Therefore, assessment and prediction of road deterioration [5] as well as establishment of the relationship between maintenance schemes and corresponding influencing factors [6] are crucial in decision making regarding pavement maintenance and fund allocation. Recently, various decision-making methods for pavement maintenance have been proposed. These methods generally aim at improving quality and reducing costs under diverse scenarios. The most common methods include the analytic hierarchy process (AHP), decision making based on a clustering algorithm, and decision making based on a genetic algorithm (GA). The AHP method was first used for decision making in pavement maintenance by [PERSON] [7]. [PERSON] and [PERSON] proposed a pavement maintenance prioritization method based on AHP and developed a three-level hierarchy model to determine the priority ranking of different pavement maintenance schemes [8]. [PERSON] et al. developed an AHP-based pavement maintenance priority model by assigning weights to the factors influencing pavement conditions [9]. However, the selection of the indices and the determination of the index weights in the AHP method remained subjective. Thus, the method of selecting an optimal solution from all available plans determines the effectiveness of the final decision. In recent years, with the development of machine learning and corresponding algorithms, some researchers began studying pavement maintenance decisions based on machine learning. [PERSON] et al. proposed a decision-making framework for pavement maintenance that combined a clustering algorithm with the PageRank algorithm [10]. In this framework, the pavements are first grouped into clusters by the clustering algorithm. Then, the maintenance ranking of the clustered road samples is determined according to the road conditions. [PERSON] et al. proposed a decision-making algorithm for pavement maintenance based on pattern recognition [11]. This method helps determine the optimal maintenance and repair plans for low-volume paved roads.