# MILP-Based Approach for High-Altitude Region Pavement Maintenance Decision Optimization
## 5 Case Analysis  
### Case Description  
To analyze the effectiveness of the pavement maintenance decision model, this paper conducted a simulation analysis using pavement maintenance data from high-altitude regions obtained through surveys. Specifically, codes were written in Python language, and the Gurobi solver was utilized to solve the optimization models, with the termination condition set to a 0.1% gap between the upper and lower bounds. The hardware used was a laptop equipped with a Win 10 system, an Intel(R) Core (TM) i7-10750H CPU (Interl, Santa Clara, CA, USA), and 32 GB of memory. In the simulation analysis, we focused on 30 road segments, starting from their state at the end of 2023 and aiming at developing a maintenance plan for the next three years. Each year includes 12 months, with April to October being suitable for maintenance and November to March being unsuitable. Each road segment has the corresponding predicted traffic flow data and an initial PCI value, with initial PCI values ranging from 63 to 86. The annual average daily traffic (AADT) and monthly traffic volume for each road segment were recorded. Specifically, the basic information of the 30 road segments is shown in Table 6, and the predicted monthly traffic volume of each segment in 2024 is shown in Figure 2.  
Regarding the maintenance approaches, we classified them into five categories and statistically analyzed their related attributes based on survey data, as shown in Table 7. The five maintenance approaches are filling cracks, patching potholes, chip seal coating or using a slurry seal, resurfacing with partial reconstruction, and complete reconstruction [1]. These approaches cover different levels from minor repairs to major maintenance. When adopting different maintenance approaches, the required working hours, protection days, and costs per unit area vary, and these model parameters are set based on the survey data. For carbon emissions, considering the impact of altitude, the settings are based on Refs. [29; 30]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Strategy**} & \multirow{2}{*}{**Objective**} & \multicolumn{6}{c}{**Weight Ranges**} \\ \cline{3-6}  & & \(\mathbf{w_{1}}\) & \(\mathbf{w_{2}}\) & \(\mathbf{w_{3}}\) & \(\mathbf{w_{4}}\) & \(\mathbf{w_{5}}\) \\ \hline
1 & Optimize maintenance effectiveness & \textgreater{}0 & \textgreater{}0 & \textgreater{}0 & \textgreater{}0 & \textgreater{}0 \\
2 & Optimize cost savings & \textgreater{}0 & \textgreater{}0 & \textgreater{}0 & \textgreater{}0 & \textgreater{}0 \\
3 & Optimize affected traffic volume & \textgreater{}0 & \textgreater{}0 & \textgreater{}0 & \textgreater{}0 & \textgreater{}0 \\
4 & Optimize environmental protection & \textgreater{}0 & \textgreater{}0 & \textgreater{}0 & \textgreater{}0 & \textgreater{}0 \\
5 & Balance all the above considerations & \textgreater{}0 & \textgreater{}0 & \textgreater{}0 & \textgreater{}0 & \textgreater{}0 \\ \hline \end{tabular}
\end{table} 
Table 5: Weight ranges for sub-objectives corresponding to different decision strategies. Figure 2: Distribution of average daily traffic flow by month.