JACIII Vol.14 No.5 pp. 487-496
doi: 10.20965/jaciii.2010.p0487


A Double-Deck Elevator Systems Controller with Idle Cage Assignment Algorithm Using Genetic Network Programming

Shingo Mabu, Lu Yu, Jin Zhou, Shinji Eto,
and Kotaro Hirasawa

Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan

October 9, 2009
April 22, 2010
July 20, 2010
double-deck elevator systems, evolutionary computation, genetic network programming

So far, many studies on Double-Deck Elevator Systems (DDES) have been done for exploring more efficient algorithms to improve the system transportation capacity, especially in a heavy traffic mode. The main idea of these algorithms is to decrease the number of stops during a round trip by grouping the passengers with the same destination as much as possible. Unlike what occurs in this mode, where all cages almost always keep moving, there is the case, where some cages become idle in a light traffic mode. Therefore, how to dispatch these idle cages, which is seldom considered in the heavy traffic mode, becomes important when developing the controller of DDES. In this paper, we propose a DDES controller with idle cage assignment algorithm embedded using Genetic Network Programming (GNP) for a light traffic mode, which is based on a timer and event-driven hybrid model. To verify the efficiency and effectiveness of the proposed method, some experiments have been done under a special down-peak pattern where passengers emerge especially at the 7th floor. Simulation results show that the proposed method improves the performance compared with the case when the cage assignment algorithm is not employed and works better than six other heuristic methods in a light traffic mode.

Cite this article as:
Shingo Mabu, Lu Yu, Jin Zhou, Shinji Eto, and
and Kotaro Hirasawa, “A Double-Deck Elevator Systems Controller with Idle Cage Assignment Algorithm Using Genetic Network Programming,” J. Adv. Comput. Intell. Intell. Inform., Vol.14, No.5, pp. 487-496, 2010.
Data files:
  1. [1] M.-L. Siikonen, “Double-deck elevators: Savings in Time and Space,” Elevator World, Vol.46 No.7, pp. 65-69, 1998.
  2. [2] H. Aoki and K. Sasaki, “Group supervisory control system assisted by artificial intelligence,” Elevator World, pp. 70-80, Feb. 1990.
  3. [3] C. B. Kim, K. A. Seong, H. Lee-Kwang, J. O. Kim, and Y. B. Lim, “A fuzzy approach to elevator group control system,” IEEE Trans. Syst., Man, Cybern., Vol.25, pp. 985-990, 1995.
  4. [4] D. Levy, M. Yadin, and A. Alexandrovitz, “Optimal control of elevators,” Int. J. Syst. Sci., Vol.8, No.3, pp. 301-320, 1977.
  5. [5] Y. W. Ho and L. C. Fu, “Dynamic Scheduling Approach to Group Control of Elevator Systems with Learning Ability,” In Proc. of the 2000 IEEE Int. Conf. on Robotics and Automation, San Francisco, CA, pp. 2410-2415, April 2000.
  6. [6] J. Sorsa, M.-L. Siikonen, and H. Ehtamo, “Optimal control of double-deck elevator group using genetic algorithm,” Int. Trans. in Operations Research, Vol.10, No.3, pp. 103-114, 2003.
  7. [7] J. Zhou, L. Yu, S. Mabu, K. Hirasawa, J. Hu, and S. Markon, “Double-deck Elevator Systems using Genetic Network Programming with Reinforcement Learning,” In Proc. of 2007 IEEE Congress on Evolutionary Computation (CEC2007), pp. 2025-2031, Singapore, Sept. 2007.
  8. [8] K. Hirasawa, T. Eguchi, J. Zhou, L. Yu, and S. Markon, “A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming,” IEEE Trans. on Systems, Man and Cybernetics, Part C, Vol.38, No.4, pp. 535-550, Aug. 2008.
  9. [9] J. Koehler and D. Ottiger, “An AI-based approach to destination control in elevators,” AI Magazine, Vol.23, No.3, pp. 59-79, 2002.
  10. [10] S. Tanaka, Y. Innami, and M. Araki, “A study on objective functions for dynamic operation optimization of a single-car elevator system with destination hall call registration,” In Proc. IEEE Int. Conf. on Systems, Man and Cyvernetics, 2004.
  11. [11] T. Eguchi, K. Hirasawa, J. Hu, and N. Ota, “A Study of Evolutionary Multiagent Models Based on Symbiosis,” IEEE trans. on Systems, Man and Cybernetics, Part B, Vol.35, No.1, pp. 179-193, Feb. 2006.
  12. [12] 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.
  13. [13] G. Barney, “Elevator Technology,” Ellis Horwood Limited, West Sussex, England, 1986.
  14. [14] G. Barney, “Elevator Traffic Handbook,” Spon Press, 2003.
  15. [15] R. D. Peters, “The theory and practice of general analysis of lift calculations,” In Proc. of the ELEVCON 1992, pp. 197-206, 1992.
  16. [16] G. R. Strakosch, “Vertical Transportation: Elevators and Escalator,” New York: Wiley, 1983.
  17. [17] K. Sasaki, S. Markon, and M. Nakagawa, “Elevator group supervisory control system using neural networks,” Elevator World, No.2, pp. 81-86, 1996.

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

Last updated on Mar. 05, 2021