MULTIPLE JOB SCHEDULING WITH ARTIFICIAL NEURAL NETWORKS

This paper presents an application of neural networks in a multiple task scheduling problem. We take the crossbar Hopfield network which is used to solve the classical traveling salesman problem and extend it to a 3-D neuro-box network (NBN) to solve multiple task scheduling on multiple servers. The approach is presented in several stages starting with a brief review of the Hopfield network, the formulation of the traveling salesman problem on the Hopfield network, the extension to the multiple traveling salesman problem, and the formulation of the manufacturing task scheduling problem, in increasing order of difficulty. At every step, the topology of the network, the energy function (or the cost function which is to be minimized) of the network, the differential equations defining the characteristics of the neurons and illustrative simulations are presented in the paper.