Comparison of conventional and neural network heuristics for job shop scheduling

Vladimir Cherkassky; Deming N. Zhou

doi:10.1117/12.140142

Comparison of conventional and neural network heuristics for job shop scheduling

Vladimir Cherkassky, Deming N. Zhou

Electrical and Computer Engineering

Research output: Contribution to journal › Conference article › peer-review

3 Scopus citations

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 to obtain better scheduling solutions than commonly used heuristics, especially for large problems. Key words: Heuristic, job shop scheduling, neural network, scaling.

Original language	English (US)
Pages (from-to)	815-825
Number of pages	11
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	1710
DOIs	https://doi.org/10.1117/12.140142
State	Published - Jul 1 1992
Event	Science of Artificial Neural Networks 1992 - Orlando, United States Duration: Apr 20 1992 → …

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© 1992 Proceedings of SPIE - The International Society for Optical Engineering. All rights reserved.

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Comparison of conventional and neural network heuristics for job shop scheduling

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