Comparison of conventional and neural network heuristics for job shop scheduling

Vladimir Cherkassky, Deming N. Zhou

Research output: Contribution to journalConference articlepeer-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 languageEnglish (US)
Pages (from-to)815-825
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume1710
DOIs
StatePublished - Jul 1 1992
EventScience of Artificial Neural Networks 1992 - Orlando, United States
Duration: Apr 20 1992 → …

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