Abstract
The paper deals with scheduling problems where tasks have to be processed on more than one processor at a time. The discussed optimization problems belong, in general, to NP-hard class and it is very likely that no polynomial-time exact algorithm solving them could ever be found. Hence, a dedicated artificial neural network has been proposed as a tool for solving multiprocessor tasks scheduling problems. The paper presents the proposed neural network structure and algorithms used to train it. Efficiency of the approach has been evaluated experimentally. Examples and computational experiment results are also shown.
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Czarnowski, I., Jędrzejowicz, P. (2000). Artificial Neural Network for Multiprocessor Tasks Scheduling. In: Intelligent Information Systems. Advances in Soft Computing, vol 4. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1846-8_19
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DOI: https://doi.org/10.1007/978-3-7908-1846-8_19
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1309-8
Online ISBN: 978-3-7908-1846-8
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