Service area-based elevator group supervisory control system using GNP with RL

Jin Zhou*, Lu Yu, Shingo Mabu, Kotaro Hirasawa, Jinglu Hu, Sandor Markon

*この研究の対応する著者

研究成果: Conference contribution

抄録

Genetic Network Programming (GNP) was proposed several years ago as a new evolutionary computation method. Its unique features, such as highly compact structure, potential memory function, etc, are verified by many studies mainly on virtual world problems. Recently, GNP is also applied to some complicated real world problems like Elevator Group Supervisory Control Systems (EGSCS) and stock price prediction systems. As we know, EGSCS is a very large scale stochastic dynamic optimization problem. Due to its vast state space, significant uncertainty and numerous resource constraints such as finite car capacities and registered hall/car calls, it is hard to manage EGSCS using conventional control methods. In this paper, we propose an enhanced algorithm of EGSCS using GNP with Reinforcement Learning (RL) where an importance weight tuning method and a car assignment policy based on service area are introduced.

本文言語English
ホスト出版物のタイトル2006 SICE-ICASE International Joint Conference
ページ5967-5972
ページ数6
DOI
出版ステータスPublished - 2006 12 1
イベント2006 SICE-ICASE International Joint Conference - Busan, Korea, Republic of
継続期間: 2006 10 182006 10 21

出版物シリーズ

名前2006 SICE-ICASE International Joint Conference

Conference

Conference2006 SICE-ICASE International Joint Conference
国/地域Korea, Republic of
CityBusan
Period06/10/1806/10/21

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 制御およびシステム工学
  • 電子工学および電気工学

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