Near optimal jobshop scheduling using neural network parallel computing

Akira Hanada, Kouhei Ohnishi

研究成果: Conference contribution

9 被引用数 (Scopus)

抄録

A parallel algorithm based on the neural network model for jobshop scheduling problem is presented in this paper. In the manufacturing system, it is becoming more complex to manage operations of facilities, because of many requirements and constraints such as to increase product throughput, reduce work-in-process and keep the due date. The goal of the proposed parallel algorithm is to find a near-optimum scheduling solution for the given schedule. The proposed parallel algorithm requires N × N processing elements (neurons) where N is the number of operations. Our empirical study on the sequential shows the behavior of the system.

本文言語English
ホスト出版物のタイトルPlenary Session, Emerging Technologies, and Factory Automation
編集者 Anon
出版社Publ by IEEE
ページ315-320
ページ数6
ISBN(印刷版)0780308913
出版ステータスPublished - 1993 12 1
イベントProceedings of the 19th International Conference on Industrial Electronics, Control and Instrumentation - Maui, Hawaii, USA
継続期間: 1993 11 151993 11 18

出版物シリーズ

名前IECON Proceedings (Industrial Electronics Conference)
1

Other

OtherProceedings of the 19th International Conference on Industrial Electronics, Control and Instrumentation
CityMaui, Hawaii, USA
Period93/11/1593/11/18

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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