MILP-Based Approach for High-Altitude Region Pavement Maintenance Decision Optimization

• Published in: Applied sciences 2024-09, Vol.14 (17), p.7670
• Main Authors: Bo, Wu, Qian, Zhendong, Yu, Bo, Ren, Haisheng, Yang, Can, Zhao, Kunming, Zhang, Jiazhe
• Format: Article
• Language: English
• Subjects: Altitude; Asphalt pavements; Data envelopment analysis; Decision making; Dynamic programming; Emissions; Energy consumption; Genetic algorithms; Greenhouse gases; high-altitude regions; Integer programming; Linear programming; Maintenance costs; mixed-integer linear programming; model reconstruction; Optimization; Pareto optimum; pavement maintenance; Planning; Preventive maintenance; Roads & highways; robust optimization; Traffic flow
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14177670

Affected by climatic factors (e.g., low temperature and intense ultraviolet radiation), high-altitude regions experience numerous pavement diseases, which compromise driving safety and negatively impact user travel experience. Timely planning and execution of pavement maintenance are particularly critical. In this paper, considering the characteristics of pavement maintenance in high-altitude regions (e.g., volatility of traffic volume, seasonality of maintenance timing, and fragility of the ecological environment), we aim to derive optimal monthly maintenance plans. We develop a multi-objective nonlinear optimization model that comprehensively accounts for minimizing maintenance costs, affected traffic volume and carbon emissions, and maximizing pavement maintenance effectiveness. Utilizing linearization methods, the model is reconstructed into a typical mixed-integer linear programming (MILP) model, enabling it to be solved directly using conventional solvers. We consider five types of decision strategies to reflect the preferences of different decision-makers. Given the uncertainty of maintenance costs, we also utilize the robust optimization method based on the acceptable objective variation range (AOVR) to construct a robust optimization model and discuss the characteristics of optimistic, robust, and pessimistic solutions. The results suggest that different decision strategies show differences in the indicators of maintenance costs, affected traffic volume, carbon emissions, and pavement performance. When multiple decision objectives are comprehensively considered, the indicators are between the maximum and minimum values, which can effectively balance the decision needs of maintenance effectiveness, maintenance timing, and environmental protection. The number of maintenance workers, the requirement of the minimum pavement condition index (PCI), and the annual budget influence the maintenance planning. The obtained robust solution can somewhat overcome the conservative nature of the pessimistic solution. The method proposed in this paper helps address the complexities of pavement maintenance decisions in high-altitude regions and provides guidance for pavement maintenance decisions in such areas.