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Critical length of grade control, fixed-elevation points, and nonoverlapping of horizontal and vertical curves are three common requirements in the vertical alignment design of roads. These three forms of constraints are, however, usually not addressed in the conventional road alignment optimization analysis because of the complexity in considering them in the mathematical formulation and solution of the problem. This paper illustrates that the artificial intelligence technique of genetic algorithms can be adopted to handle these three forms of constraints effectively. The formulation of the genetic-algorithm computer program and the method of solution are explained. The validity of the optimization algorithm is verified against a dynamic programming solution. Examples are presented to illustrate the application of the genetic-algorithm program to problems involving critical length of grade requirements, fixed-elevation control, and nonoverlapping of horizontal and vertical curves. These three constraints were found to have significant effects on the computed optimal alignments and the associated construction costs. [ABSTRACT FROM AUTHOR]

There are two alternatives for production organization in precast factories—namely, the comprehensive method and the specialized method. Production scheduling under the specialized alternative has been found to be a difficult optimization problem if heterogeneous elements are involved. A flow shop sequencing model that incorporates actual constraints encountered in practice is proposed for this difficult case of precast production scheduling. The model is solved using a genetic algorithm (GA). The traditional minimize makespan and the more practical minimize tardiness penalty objective functions are optimized separately, as well as simultaneously using a normalized weighted GA. Comparisons between the GA and classical heuristic rules show that the GA can obtain good schedules for the model, giving a family of solutions that are at least as good as those produced by the use of heuristic rules. [ABSTRACT FROM AUTHOR]

This paper describes the development of a computer model (known as PAVENET-R) based on genetic algorithms, an optimization tool capable of overcoming combinatorial explosion, to solve the pavement maintenance-rehabilitation trade-off problem at the network level. The formulation of the PAVENET-R model is described in detail. An integer coding scheme is selected for parameter representation in the model. Two genetic-algorithm operators, namely the crossover operator and the mutation operator, are used. A “change table” encodes constraints to the genetic-algorithm operations to ensure that only valid offspring are generated from a parent pool. Four numerical examples of road networks of 30 pavement segments, each with different relative costs of rehabilitation and maintenance activities, are analyzed to demonstrate the trade-off relationship between pavement rehabilitation and maintenance activities. The detailed maintenance and rehabilitation schedules of the solutions, and the convergence characteristics of each solution are presented. [ABSTRACT FROM AUTHOR]

Discusses the use of genetic algorithms in vertical highway alignment analysis. Comment on the use of the term "erroneous requirement" to describe the nonoverlapping of horizontal and vertical curves; Details of 3D features involved in superimposing vertical and horizontal curvatures; Factors to consider in highway designs in relation to safety requirements.