Top > JACIII > A Study of Double-Deck Elevator Systems Using Genetic Network ...

single-jc.php

JACIII Vol.13 No.1 pp. 35-44
doi: 10.20965/jaciii.2009.p0035
(2009)

Paper:

Views over last 60 days: 504

A Study of Double-Deck Elevator Systems Using Genetic Network Programming with Reinforcement Learning

Jin Zhou*, Lu Yu*, Shingo Mabu*, Kaoru Shimada*,
Kotaro Hirasawa*, and Sandor Markon**

*Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7, Wakamatsu-ku, Kitakyushu, Fukuoka 803-0135, Japan

**FUJITEC Co.Ltd. Prod.Dev.HQ, Big Wing, Hikone, Shiga, Japan

Received:
January 17, 2008
Accepted:
May 1, 2008
Published:
January 20, 2009
Creative Commons License
Keywords:
double-deck elevator system, genetic network programming, evolutionary computation
Abstract
In order to increase the transportation capability of elevator group systems in high-rise buildings without adding elevator installation space, double-deck elevator systems (DDES) is developed as one of the next generation elevator group control systems. Artificial intelligence (AI) technologies have been employed to find some efficient solutions in the elevator group control systems during the late 20th century. Genetic Network Programming (GNP), a new evolutionary computation method, has been employed as the elevator group control system controller in some studies of recent years. Moreover, reinforcement learning (RL) has been also found to be useful for more improvements of elevator group control performances when it is combined with GNP. In this paper, we proposed a new approach of DDES using GNP with RL, and did some experiments on a simulated elevator group control system of a typical office building to evaluate its applicability and efficiency. Simulation results show that the DDES using GNP with RL performs better than the one without RL in regular and down-peak time, while both of them outperforms a conventional approach and a heuristic approach in all three traffic patterns.
Cite this article as:
J. Zhou, L. Yu, S. Mabu, K. Shimada, K. Hirasawa, and S. Markon, “A Study of Double-Deck Elevator Systems Using Genetic Network Programming with Reinforcement Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.13 No.1, pp. 35-44, 2009.
Data files:
References
  1. [1] J. Sorsa and M-.L. Siikonen, “Double-deck destination control system,” in Elevator World, pp. 318-327, 2006.
  2. [2] X. Bi, C. Zhu, and Q. Ye, “A GA-Based Approach to the Multi-Objective Optimization Problem in Elevator Group Control System,” in Elevator World, pp. 58-63, June 2004.
  3. [3] C. Kim, K. Seong, H. Lee-Kwang, and J. O. Kim, “Design and Implementation of a Fuzzy Elevator Group Control System,” IEEE Trans. on System, Man and Cybernetics, PART-A, Vol.28, No.3, pp. 277-287, 1998.
  4. [4] H. Wan, C. Liu, and H. Liu, “NN Elevator Group-Control Method,” in Elevator World, pp. 149-154, February 2003.
  5. [5] K. Hirasawa, T. Eguchi, J. Zhou, L. Yu, J. Hu, and S. Markon, “A Double-deck Elevator Group Supervisory Control System using Genetic Network Programming,” IEEE Transactions on Systems, Man, and Cybernetics, PART-C, Vol.38, No.4, pp. 535-550, 2008/7.
  6. [6] S. Mabu, K. Hirasawa, and J. Hu, “A Graph-Based Evolutionary Algorithm: Genetic Network Programming and Its Extension Using Reinforcement Learning,” Evolutionary Computation, MIT Press, Vol.15, No.3, pp. 369-398, 2007.
  7. [7] J. Zhou, T. Eguchi, S. Mabu, K. Hirasawa, J. Hu, and S. Markon, “A study of applying Genetic Network Programming with Reinforcement Learning to Elevator Group Supervisory Control System,” in Proc. of IEEE Congress on Evolutionary Computation (CEC2006), pp. 10392-10398, 2006/7.
  8. [8] G. Barney and S. dos Santos, “Elevator Traffic Analysis, Design and Control,” second ed., Peter Peregrinus Ltd, London, 1985.
  9. [9] G. Barney, “Elevator Traffic Handbook,” Spon Press, 2003.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024