Calculating Weight Matrix of Neural Network for Resource Leveling

Abstract

In this paper, a new approach for the computation of the weight matrix of a Hopfield neural network for resource leveling is introduced. The proposed method achieves significantly improved efficiency over the conventional technique of employing the functional expressions of the weights by exploiting the structural properties of the matrices arising in the formulation of the resource leveling problem as a quadratic zero-one optimization. These structural properties are identified and stated in terms of template-matrix contributions of the cost and constraint functions of the quadratic optimization, to the weight matrix of the Hopfield neural network. It is shown that by using these templates, the weight matrix can be filled in directly, based on the early start schedule of a project.