A random neural network approach to an assets to tasks assignment problem
Erol Gelenbe, Stelios Timotheou, David Nicholson · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
We investigate the assignment of assets to tasks where each asset can potentially execute any of the tasks, but assets execute tasks with a probabilistic outcome of success. There is a cost associated with each possible assignment of an asset to a task, and if a task is not executed there is also a cost associated with the nonexecution of the task. Thus any assignment of assets to tasks will result in an expected overall cost which we wish to minimise. We propose an approach based on the Random Neural Network (RNN) which is fast and of low polynomial complexity. The evaluation indicates that the proposed RNN approach comes at most within 10% of the cost obtained by the optimal solution in all cases.