Multiobjective optimization for pavement maintenance programming

Pavement maintenance planning and programming requires optimization analysi s involving multiobjective considerations. Traditionally single-objective o ptimization techniques have been employed by pavement researchers and pract itioners because of the complexity involved in multiobjective analysis. Thi s paper develops a genetic-algorithm-based procedure for solving multiobjec tive network level pavement maintenance programming problems. The concepts of Pareto optimal solution set and rank-based fitness evaluation, and two m ethods of selecting an optimal solution, were adopted. It was found that th e robust search characteristics and multiple-solution handling capability o f genetic-algorithms were well suited for multiobjective optimization analy sis. Formulation and development of the solution algorithm were described a nd demonstrated with a numerical example problem in which a hypothetical ne twork level pavement maintenance programming analysis was performed for two - and three-objective optimization, respectively. A comparison between the two- and three-objective solutions was made to highlight some practical con siderations in applying multiobjective optimization to pavement maintenance management.