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Proceedings Paper

Comparison of conventional and neural network heuristics for job shop scheduling
Author(s): Vladimir Cherkassky; Deming Norman Zhou
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Paper Abstract

A new neural network for solving job shop scheduling problems is presented. The proposed scaling neural network (SNN) achieves good (linear) scaling properties by employing nonlinear processing in the feedback connections. Extensive comparisons between SNN and conventional heuristics for scheduling are presented. These comparisons indicate that the proposed SNN allows better scheduling solutions than commonly used heuristics, especially for large problems.

Paper Details

Date Published: 1 July 1992
PDF: 11 pages
Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140142
Show Author Affiliations
Vladimir Cherkassky, Univ. of Minnesota/Twin Cities (United States)
Deming Norman Zhou, Univ. of Wisconsin/Stout (United States)

Published in SPIE Proceedings Vol. 1710:
Science of Artificial Neural Networks
Dennis W. Ruck, Editor(s)

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