A double-deck elevator group supervisory control system using genetic network programming

Kotaro Hirasawa*, Toru Eguchi, Zhou J. Zhou, Lu Yu, Jinglu Hu, Sandor Markon


研究成果: Article査読

153 被引用数 (Scopus)


Elevator group supervisory control systems (EGSCSs) are designed so that the movement of several elevators in a building is controlled efficiently. The efficient control of EGSCSs using conventional control methods is very difficult due to its complexity, so it is becoming popular to introduce artificial intelligence (AI) technologies into EGSCSs in recent years. As a new approach, a graph-based evolutionary method named genetic network programming (GNP) has been applied to the EGSCSs, and its effectiveness is clarified. The GNP can introduce various a priori knowledge of the EGSCSs in its node functions easily, and can execute an efficient rule-based group supervisory control that is optimized in an evolutionary way. Meanwhile, double-deck elevator systems (DDESs) where two cages are connected in a shaft have been developed for the rising demand of more efficient transport of passengers in high-rise buildings. The DDESs have specific features due to the connection of cages and the need for comfortable riding; so its group supervisory control becomes more complex and requires more efficient group control systems than the conventional single-deck elevator systems (SDESs). In this paper, a new group supervisory control system for DDESs using GNP is proposed, and its optimization and performance evaluation are done through simulations. First, optimization of the GNP for DDSEs is executed. Second, the performance of the proposed method is evaluated by comparison with conventional methods, and the obtained control rules in GNP are studied. Finally, the reduction of space requirements compared with SDESs is confirmed.

ジャーナルIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
出版ステータスPublished - 2008 7月

ASJC Scopus subject areas

  • 制御およびシステム工学
  • ソフトウェア
  • 情報システム
  • 人間とコンピュータの相互作用
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学


「A double-deck elevator group supervisory control system using genetic network programming」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。