An unsupervised neural network to solve transportation problems

Abstract

This article presents a new two-layer neural network model for predicting the optimum solution to linear programming problems. An energy function that transforms linear programming problem into a non-linear function is developed from the objective and constraints. The learning rule, based on gradient descent algorithm, is employed to get the appropriate weight structure of the neural network. The network is tested with different examples including a transportation problem. The results are compared along with the available solutions.