Comparison of conventional and neural network heuristics for job shop scheduling

Vladimir Cherkassky; Deming Norman Zhou

doi:10.1117/12.140142

1 July 1992 Comparison of conventional and neural network heuristics for job shop scheduling

Vladimir Cherkassky, Deming Norman Zhou

Proceedings Volume 1710, Science of Artificial Neural Networks; (1992) https://doi.org/10.1117/12.140142
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

Vladimir Cherkassky and Deming Norman Zhou "Comparison of conventional and neural network heuristics for job shop scheduling", Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); https://doi.org/10.1117/12.140142

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