Genetic algorithms (GAs) are becoming an increasingly popular way to search huge solution spaces to find good solutions. Pavement management problems are specialized scheduling problems for which the solution space grows exponentially with the problem size so that the solution space size becomes unmanageable by “true” optimization techniques very quickly. Pavement management is thus ideally suited for directed random search heuristics such as GAs. The formulation of a typical general project-level pavement management problem for solution by GAs is described. Both the single- and the multiobjective cases are discussed, and the results of a series of tests of the performance of the formulations are presented. A very useful insight is then presented to show how the general network problem can be solved using project-efficient surfaces. It is concluded that GAs are an extremely flexible and robust approach to solving myriad forms of pavement management problems and provide a rich area for future research. It is also concluded that using efficient surfaces to break down the network problem into project subproblems holds a great deal of promise for overcoming some of the existing problems of optimization in pavement management.
The majority of maintenance optimization literature is focused on pavement maintenance related to pavement management systems. The literature does not cover well the proper procedures for optimizing the full range of typical highway maintenance activities based on measurement and prediction of performance. In addition, practitioners are only now getting started in this area of maintenance optimization, in which they use software tools to aid in planning non-pavement—related maintenance. Although much of the optimization work to date focused on predicting deterioration explicitly over time, the method of maintenance optimization that is presented in this paper, called maintenance analysis, assumes a steady state is reached with regard to maintenance activities. This maintenance analysis method allows maintenance managers to use level of service and utility functions to define maintenance indices. With this method, managers are able to identify and plan the optimal mix of maintenance activities to (a) maximize performance with respect to maintenance performance indices based on user-defined budget constraints and (b) minimize cost based on level of service targets. A simple numerical example is presented to illustrate the method.
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