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

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.

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Publication Details

Journal
Applied Sciences
Published
2024-08-30
DOI
https://doi.org/10.3390/app14177670
Citations
2
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
Field-Weighted Citation Impact
0.52

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article

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

Bo Wu, Kunming Zhao, Haisheng Ren, Jiazhe Zhang et al.
2 citations
Applied Sciences
Infrastructure Maintenance and Monitoring
0.52
article

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

Bo Wu, Kunming Zhao, Haisheng Ren, Jiazhe Zhang, Zhendong Qian, Can Yang, Bo Yu
article en
2 citations

Abstract

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.

Applied SciencesVol. 14(17)
Tibet University (CN), Southeast University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 36%
Infrastructure Maintenance and Monitoring
0.52
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