# MILP-Based Approach for High-Altitude Region Pavement Maintenance Decision Optimization
## 1 Introduction
### Literature Review  
In the previous literature, [PERSON] et al. [1], [PERSON] et al. [3], and [PERSON] et al. [6] have reviewed some work on pavement maintenance decisions. This paper further reviews these two perspectives, project-level and network-level [7], and Table 1 intuitively summarizes the characteristics described in some related literature. Project-level pavement maintenance decision-making primarily focuses on developing maintenance plans, optimizing budgets, and evaluating maintenance effects for a specific road segment. Detailed data are used to formulate a specific maintenance plan, emphasizing maximizing service levels and lifespan of the segment under limited resources. [PERSON] et al. [8] discussed the multi-objective optimization of project-level pavement maintenance plans, constructing an optimization model integrating pavement performance, costs, and environmental impacts, and designing the corresponding genetic algorithm (GA). [PERSON] et al. [9] focused on the integrated optimization of timing and strategies for preventive maintenance of asphalt pavements based on periods, constructing an integrated optimization approach for preventive maintenance timing and strategies based on the reconstructed data envelopment analysis (DEA). [PERSON] et al. [10] proposed a preventive maintenance decision method for asphalt pavements considering multiple damage characteristics by constructing a BP neural network model that combines dominant damage and overall damage. [PERSON] et al. [11] addressed the optimization of pavement maintenance decisions under dual constraints of funding and effectiveness, proposing a multi-annual project-level maintenance planning decision process. The project-level pavement maintenance decision-making is relatively meticulous, but this approach cannot carry out a comprehensive decision at the network level, which is a practical demand. Network-level pavement maintenance decisions can compensate for this shortcoming, involving multiple road systems within a region. The primary goal of the network-level maintenance decision is to determine the road segments within the network that need maintenance and allocate appropriate maintenance methods and timings. [PERSON] et al. [12] discussed the optimization of multi-objective pavement maintenance decision plans, proposing a parameter-free discrete particle swarm algorithm. [PERSON] et al. [1] constructed an optimization model considering the needs of the government, highway agencies, contractors, and common users for pavement maintenance and rehabilitation management systems. [PERSON] et al. [13] discussed the multi-objective optimization problem of network-level pavement maintenance and rehabilitation programming, constructing an integer programming model to optimize road conditions, user disruption costs, and agency costs, and proposing a Pareto-based optimal trade-off method. [PERSON] et al. [14] combined the generalized differential evolution 3 (GDE3) algorithm, life cycle assessment (LCA), and life cycle cost analysis (LCCA) to construct a multi-objective decision model for highway network maintenance. [PERSON] et al. [15] discussed the optimal pavement management strategy to reduce greenhouse gas emissions under budget constraints, constructed an optimization model considering budget constraints and minimizing greenhouse gas emissions, and designed a solution algorithm based on Lagrangian relaxation and dynamic programming. [PERSON] et al. (2016) constructed a performance prediction model based on the Markov chain model, focusing on maximizing the overall condition of the road network while minimizing maintenance and renovation costs. [PERSON] et al. (2017) discussed the optimization of pavement maintenance and rehabilitation planning under budget and pavement deterioration uncertainties and constructed a multi-stage stochastic mixed-integer programming model. [PERSON] et al.