A random neural network approach to an assets to tasks assignment problem

Erol Gelenbe; Stelios Timotheou; David Nicholson

doi:10.1117/12.840494

27 April 2010 A random neural network approach to an assets to tasks assignment problem

Erol Gelenbe, Stelios Timotheou, David Nicholson

Proceedings Volume 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX; 76970Q (2010) https://doi.org/10.1117/12.840494
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Erol Gelenbe, Stelios Timotheou, and David Nicholson "A random neural network approach to an assets to tasks assignment problem", Proc. SPIE 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX, 76970Q (27 April 2010); https://doi.org/10.1117/12.840494

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