Optimization of air transport logistics by genetic algorithms
Siegfried Eisinger and
Risk, Decision and Policy, 2002, vol. 7, issue 1, 7-23
Genetic algorithm search techniques provide an innovative and robust means of optimization in complex, multivariate real-scale problems. In this paper we present an application of genetic algorithms to the optimization of airport operation and development for an increasing traffic situation. The approach is proven successful and much less time consuming compared to traditional what-if analysis. In addition to the base case optimization results, sensitivity analyses both with respect to the economic parameters of the fitness function, subject to the optimization, and with respect to some important genetic algorithm settings have been performed and yield consistent results.
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