Road-maintenance planning using genetic algorithms. I: Formulation

Abstract

The present paper demonstrates the applicability of genetic algorithms, as an optimization tool capable of overcoming combinatorial explosion, to the road-maintenance planning problem at the network level. Genetic algorithms are search algorithms based upon the principles of Darwinian evolution. The concept of the survival of the fittest is used in a structured, yet randomized, information exchange to form a robust search algorithm. Genetic algorithms efficiently exploit historical information to locate search points with improved performance. The theoretical basis and operations of genetic algorithms are presented. A computer model, PAVENET, formulated on the operating principles of genetic algorithms to serve as an analytical aid for pavement maintenance engineers, is introduced. The formulation of the PAVENET model is described in detail. Analyses are conducted to show the characteristics of important operating parameters of the PAVENET program. These parameters include: (1) Parent pool size; (2) mutation rate in offspring generation; and (3) ranking system for offspring selection. The convergence process of a sample problem as analyzed by the PAVENET program is studied and recommendations on the choice of operating parameters are made.