Rule driven multi objective dynamic scheduling by data envelopment analysis and reinforcement learning

Xili Chen, Xinchang Hao, Hao Wen Lin, Tomohiro Murata

研究成果: Conference contribution

14 被引用数 (Scopus)

抄録

This paper presents a rule driven method of developing composite dispatching rule for multi objective dynamic scheduling. Data envelopment analysis is adopted to select elementary dispatching rules, where each rule is justified as efficient for optimizing specific operational objectives of interest. The selected rules are subsequently combined into a single composite rule using the weighted aggregation manner. An intelligent agent is trained using reinforcement learning to acquire the scheduling knowledge of assigning the appropriate weighting values for building the composite rule to cope with the WIP fluctuation of a machine. Implementation of the proposed method in a two objective dynamic job shop scheduling problem is demonstrated and the results are satisfactory.

本文言語English
ホスト出版物のタイトル2010 IEEE International Conference on Automation and Logistics, ICAL 2010
ページ396-401
ページ数6
DOI
出版ステータスPublished - 2010 11 17
イベント2010 IEEE International Conference on Automation and Logistics, ICAL 2010 - Shatin, Hong Kong
継続期間: 2010 8 162010 8 20

出版物シリーズ

名前2010 IEEE International Conference on Automation and Logistics, ICAL 2010

Conference

Conference2010 IEEE International Conference on Automation and Logistics, ICAL 2010
国/地域Hong Kong
CityShatin
Period10/8/1610/8/20

ASJC Scopus subject areas

  • 制御およびシステム工学

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