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

Paper:

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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
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