Pavement maintenance and rehabilitation programming requires the consideration of conflicting objectives in order to optimize its life-cycle costs. While there are several approaches to solve multi-objective problems for pavement management systems, when user costs or environmental impacts are considered, the optimal solutions are often unpractical to be accepted by road agencies, given the dominating share of user costs in the total life-cycle costs.

This paper presents a two-stage optimization methodology that considers maximization of pavement quality and minimization of agency costs as the objectives to be optimized at the pavement section level, while at the network level, the objectives are to minimize agency and user costs. The main goal of this approach is to provide decision-makers with a range of optimal solutions from which a practically implementable one could be selected by the agency concerned. A sensitivity analysis and some trade-off graphics illustrate the importance in balancing all the objectives in order to obtain reasonable solutions for highway agencies. The multi-objective optimization problems at both levels are solved using genetic algorithms. The results of a case study indicate the applicability of the methodology.