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

Kotaro Hirasawa, Toru Eguchi, Zhou J. Zhou, Lu Yu, Takayuki Furuzuki, Sandor Markon

Research output: Contribution to journalArticle

144 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)535-550
Number of pages16
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Volume38
Issue number4
DOIs
Publication statusPublished - 2008 Jul

Fingerprint

Elevators
Control systems
Artificial intelligence

Keywords

  • Double-deck elevator
  • Elevator group supervisory control systems (EGSCS)
  • Evolutionary optimization
  • Genetic network programming (GNP)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Human-Computer Interaction
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

A double-deck elevator group supervisory control system using genetic network programming. / Hirasawa, Kotaro; Eguchi, Toru; Zhou, Zhou J.; Yu, Lu; Furuzuki, Takayuki; Markon, Sandor.

In: IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, Vol. 38, No. 4, 07.2008, p. 535-550.

Research output: Contribution to journalArticle

@article{7bc9fa96de0c4a37a12f9fa802068315,
title = "A double-deck elevator group supervisory control system using genetic network programming",
abstract = "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.",
keywords = "Double-deck elevator, Elevator group supervisory control systems (EGSCS), Evolutionary optimization, Genetic network programming (GNP)",
author = "Kotaro Hirasawa and Toru Eguchi and Zhou, {Zhou J.} and Lu Yu and Takayuki Furuzuki and Sandor Markon",
year = "2008",
month = "7",
doi = "10.1109/TSMCC.2007.913904",
language = "English",
volume = "38",
pages = "535--550",
journal = "IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews",
issn = "1094-6977",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

TY - JOUR

T1 - A double-deck elevator group supervisory control system using genetic network programming

AU - Hirasawa, Kotaro

AU - Eguchi, Toru

AU - Zhou, Zhou J.

AU - Yu, Lu

AU - Furuzuki, Takayuki

AU - Markon, Sandor

PY - 2008/7

Y1 - 2008/7

N2 - 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.

AB - 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.

KW - Double-deck elevator

KW - Elevator group supervisory control systems (EGSCS)

KW - Evolutionary optimization

KW - Genetic network programming (GNP)

UR - http://www.scopus.com/inward/record.url?scp=46849110563&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=46849110563&partnerID=8YFLogxK

U2 - 10.1109/TSMCC.2007.913904

DO - 10.1109/TSMCC.2007.913904

M3 - Article

AN - SCOPUS:46849110563

VL - 38

SP - 535

EP - 550

JO - IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews

JF - IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews

SN - 1094-6977

IS - 4

ER -