Faster Converging Global Heuristic for Continuous Network Design Using Radial Basis Functions

Abstract

In light of the demand for more complex network models and general solution methods, this research introduces a radial basis function-based method as a faster alternative global heuristic to a genetic algorithm method for the continuous network design problem. Two versions of the algorithm are tested against the genetic algorithm in three experiments: the Sioux Falls, South Dakota, network with standard origin–destination flows; the same network with double the flows to test performance under a more congested scenario; and an illustrative experiment with the Anaheim, California, network to compare the scalability of performance. To perform the experiments, parameters for the network design problem were developed for the Anaheim network. The Anaheim test would be the first instance of testing the radial basis function methods on a 31-dimensional network design problem. Results indicate that the multistart local radial basis function method performs notably better than the genetic algorithm in all three experiments and would therefore be an attractive method to apply to more complicated network design models involving larger networks and more complex constraints, objectives, and representations of the time dimension.