The resolution of an open-loop resource allocation problem using a neural network approach

A neural network-based optimization algorithm to solve an open-loop resource allocation problem is presented. The approach used is well suited to represent the structure of the model in which the occurrence of asynchronous outcome and decision events are explicitly incorporated. Mainly inspired from the principles of Hopfield neural networks, the algorithm computes a near-optimal solution to the illuminator scheduling problem while maintaining constraint satisfaction to support weapon-target allocation. A computational experiment conducted within the context of naval anti-air warfare shows the strengths and weaknesses of the proposed method over an alternate greedy technique