Multiobjective Optimization in Pavement Management System Using NSGA-II Method

Abstract

The performance of multiobjective optimization analysis with respect to the multidimensional nature of pavement management systems can be improved. In this paper, a deterministic multiobjective optimization model for flexible pavement management systems is introduced at the network level for three purposes: minimizing the cost of agency, minimizing user costs, and maximizing the residual value of pavements at the end of the analysis period. A nondominated sorting genetic algorithm (NSGA-II) was used to solve the model. The Texas pavement performance prediction model was applied in order to predict the pavement condition. To calculate the road user costs, the HDM-4 road user costs model was calibrated. The model was implemented on a road network with a total length of 85 km. The results showed that the best answer from the Pareto optimal solutions was a set of weights corresponding to WAC=.25, WUC=.7, and WRV=.5. The reason for the higher weight of the road user costs was the difference between the minimum and maximum of these types of costs, which are significantly higher than other costs.