A Study on the Application of Genetic Algorithms to the Optimization of Road Maintenance Strategies

This study proposes an optimization method that considers both section resurfacing and localized repairs of damaged points, based on the highly uneven distribution of pavement distress locations and under budget constraints. The model is formulated as an MILP (mixed-integer linear programming) model, where binary variables are used to simultaneously determine section resurfacing and localized repair actions. To overcome the excessive computation time required for large-scale road networks, a GA (genetic algorithm) is designed to perform heuristic searches. The model fully integrates information on distress locations, deduction values, and repair costs, and uses the maximization of the average PCI (pavement condition index) as the objective to ensure that decision-making focuses on tangible improvements in pavement service levels. Compared with the traditional point-by-point repair strategy, the proposed method can further increase the average PCI by approximately 3.5–4.0 points under medium- to high-budget conditions, demonstrating significant quantitative benefits. By simultaneously integrating section resurfacing and localized repair decisions, it saves about 10–15% more resources than individual repair methods while ensuring higher coverage of pavement distress. This method provides road maintenance agencies with a quantitative tool to flexibly allocate resurfacing and localized repair strategies under limited budgets.