Recurrent neural network and genetic algorithm approaches for a dual route optimization problem: A real case study

Abstract

This paper describes a real case study has been considered. It presents a dual optimization problem that consists in finding the optimal routes in the called principal and capillary routes. The problem has been considered as a travel salesman problem with time windows (TSPTW). The restrictions of Miller et al. have been used in order to reduce the computational cost [56]. A recurrent neural network approach is employed, which involves not just unsupervised learning to train neurons, but an integrated approach where Genetic Algorithm is utilized for training neurons so as to obtain a model with the least error. © 2013 Springer-Verlag.