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Proceedings Paper

A random neural network approach to an assets to tasks assignment problem
Author(s): Erol Gelenbe; Stelios Timotheou; David Nicholson
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Paper 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.

Paper Details

Date Published: 27 April 2010
PDF: 9 pages
Proc. SPIE 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX, 76970Q (27 April 2010); doi: 10.1117/12.840494
Show Author Affiliations
Erol Gelenbe, Imperial College London (United Kingdom)
Stelios Timotheou, Imperial College London (United Kingdom)
David Nicholson, BAE Systems Ltd. (United Kingdom)

Published in SPIE Proceedings Vol. 7697:
Signal Processing, Sensor Fusion, and Target Recognition XIX
Ivan Kadar, Editor(s)

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