# MILP-Based Approach for High-Altitude Region Pavement Maintenance Decision Optimization
## 5 Case Analysis
### Case Description
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Segment** & **Length/m** & **Width/m** & **AADT** & \
\begin{tabular}{c} **Initial** \\ **PCI** \\ \end{tabular} & **Segment** & **Length/m** & **Width/m** & **AADT** &
\begin{tabular}{c} **Initial** \\ **PCI** \\ \end{tabular} \\ \hline
01 & 180 & 4.5 & 6000 & 78 & 16 & 810 & 3.5 & 3560 & 81 \\
02 & 190 & 3.5 & 5200 & 80 & 17 & 220 & 3 & 12,400 & 82 \\
03 & 400 & 4 & 7200 & 79 & 18 & 320 & 3.5 & 9200 & 75 \\
04 & 260 & 3.5 & 5200 & 78 & 19 & 100 & 3 & 12,000 & 76 \\
05 & 300 & 4 & 8800 & 81 & 20 & 80 & 4 & 9600 & 82 \\
06 & 320 & 3 & 7200 & 78 & 21 & 920 & 3.5 & 7600 & 75 \\
07 & 280 & 3 & 7200 & 81 & 22 & 610 & 3 & 6800 & 77 \\
08 & 90 & 3.5 & 6400 & 76 & 23 & 230 & 3.5 & 6000 & 81 \\
09 & 70 & 3 & 5600 & 81 & 24 & 340 & 4 & 5600 & 76 \\
10 & 288 & 3 & 8400 & 78 & 25 & 550 & 4.5 & 8400 & 80 \\
11 & 165 & 4.5 & 10,000 & 75 & 26 & 290 & 3 & 3840 & 78 \\
12 & 180 & 3.5 & 6800 & 80 & 27 & 280 & 3.5 & 9200 & 80 \\
13 & 580 & 3 & 8000 & 82 & 28 & 650 & 3.5 & 6800 & 77 \\
14 & 620 & 3 & 7200 & 76 & 29 & 280 & 3 & 5200 & 79 \\
15 & 750 & 4 & 6400 & 79 & 30 & 660 & 3 & 4400 & 81 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistics of basic information of road segments. For other parameters, the annual budget was set to CNY 600,000, with a total budget of CNY 1,500,000. The number of available workers was 20, and the daily working hours were 8 h. Meanwhile, the average PCI value requirement for the segments was set to 76, with a minimum value of 72 and a maximum value of 100. The parameters \(\alpha_{i,1}\) and \(\beta_{i,1}\) for Constraint (1) were set to \(-0.05\) and \(-0.02\), respectively. The parameters \(\alpha_{i,2}\) and \(\beta_{i,2}\) for Constraint (4) were set to 16.074 and \(-0.026\), respectively. Additionally, the sufficiently large positive number M was set to 200. Regarding the objective functions, the five sub-objectives mentioned above were individually set as the model's decision objectives. The solutions for the five models were obtained with the above parameters. The maximum value for Objective function (14) is 19,418,050,875.175, the minimum value for Objective function (15) is 650,053, the minimum value for Objective function (16) is 856,268, the minimum value for Objective function (17) is 59.186, and the minimum value for Objective function (18) is 1,383,646. Based on this, the differences in magnitude among them can be eliminated using Equation (26). For the five decision strategies, we set the corresponding weights according to Table 8. In the simulation analysis, these five decision strategies were all discussed. Furthermore, to analyze the model's effectiveness, a baseline strategy was considered. This baseline strategy, commonly adopted in previous studies, aims for optimal effectiveness and minimal cost. It should be noted that, unlike other models, the baseline strategy's maintenance decision model assumes that maintenance can occur in any month of the year, which was the default assumption in previous research.