# MILP-Based Approach for High-Altitude Region Pavement Maintenance Decision Optimization
## 7 Conclusions  
This paper proposes a pavement maintenance optimization decision method for high-altitude areas. The goal is to obtain a monthly maintenance plan by incorporating the impact of high-altitude climates on pavement maintenance, the effects of pavement maintenance on traffic and the ecological environment, and maintenance cost factors into the optimization decision requirements. It also addresses the influence of decision preferences on related indicators of pavement maintenance plans and constructs mixed-integer linear programming (MILP) models from the perspectives of common optimization and robust optimization. The conclusions drawn from the case analysis are summarized as follows:  
Incorporating multiple decision objectives into the optimization model provides an acceptable compromise compared with considering only a single decision objective. When single or similar decision objectives are considered (e.g., Strategies 1 to 4), the optimal solutions tend to favor their corresponding decision objectives. For instance, if the goal is solely to optimize pavement maintenance effectiveness (i.e., Strategy 1), the affected traffic volume is the largest. When multiple decision objectives with conflicting relationships are considered, the multi-objective optimization method can obtain a balanced solution. For example, the maintenance plan derived from Strategy 5, which comprehensively considers various decision objectives, results in indicators that are all less than the corresponding maximum values and greater than the corresponding minimum values. Compared with previous models, incorporating the unique needs of high-altitude pavement maintenance into the optimization model and refining the maintenance timing  
Figure 7: Total maintenance cost corresponding to robust and pessimistic solutions under gradual changes in the unit maintenance cost. can yield more feasible decision plans. For example, decision models that only consider carbon emissions and costs may result in less feasible outcomes. Maintenance timing might be scheduled in months unsuitable for construction or during high-traffic periods. Equipping with suitable pavement maintenance decision premises can lead to better comprehensive maintenance effects. For instance, when the number of maintenance workers increases from 15 to 20, the affected traffic volume decreases from 276,218 pcu to 265,147 pcu, a reduction of 4%. This indicates that reasonably increasing the number of workers can effectively mitigate the impact of maintenance tasks on traffic. Robust optimization is one effective method to address uncertainties in pavement maintenance decisions. By targeting the uncertainty of unit area maintenance costs and employing a robust optimization method based on the acceptable objective variation range (AOVR), it is possible to derive a maintenance plan that considers the worst-case conditions (i.e., the least favorable unit maintenance cost) yet remains relatively positive. For example, in the case study, the optimistic solution becomes infeasible as unit area maintenance costs increase, and the robust solution outperforms the pessimistic solution in terms of affected traffic volume, carbon emissions, average PCI, and average IRI. In future research, the scope of parameters can be expanded to cover more different types of roads and environmental conditions in high-altitude areas, thereby enhancing the model's precision and applicability. Additionally, more comprehensive decision variables and constraints can be introduced into the model, such as considering dynamic changes in traffic demand and scheduling multiple maintenance activities. Moreover, attention can also be given to other environmental indicators beyond carbon emissions to more comprehensively assess the environmental impact of pavement maintenance. Additionally, considering the potential of nonlinear programming methods in handling similar problems, accurate and efficient nonlinear solutions are also worth exploring. Conceptualization, W.B. and Z.Q. ; methodology, W.B. and B.Y.