Double-deck elevator systems using genetic network programming with reinforcement learning

Jin Zhou, Lu Yu, Shingo Mabu, Kotaro Hirasawa, Takayuki Furuzuki, Sandor Markon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In order to increase the transportation capability of elevator group systems in high-rise buildings without adding elevator installation space, double-deck elevator system (DDES) is developed as one of the next generation elevator group systems. Artificial intelligence (AI) technologies have been employed to And some efficient solutions in the elevator group control systems during the late 20th century. Genetic Network Programming (GNP), a new evolutionary computation method, is reported to be employed as the elevator group system controller in some studies of recent years. Moreover, reinforcement learning (RL) is also verified to be useful for more improvements of elevator group 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 system of a typical office building to check its 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.

Original languageEnglish
Title of host publication2007 IEEE Congress on Evolutionary Computation, CEC 2007
Pages2025-2031
Number of pages7
DOIs
Publication statusPublished - 2007
Event2007 IEEE Congress on Evolutionary Computation, CEC 2007 -
Duration: 2007 Sep 252007 Sep 28

Other

Other2007 IEEE Congress on Evolutionary Computation, CEC 2007
Period07/9/2507/9/28

Fingerprint

Network Programming
Genetic Network
Elevators
Reinforcement learning
Reinforcement Learning
Genetic Programming
Evolutionary Computation
Efficient Solution
Artificial Intelligence
Office buildings
Control System
Traffic
Heuristics
Evolutionary algorithms
Controller
Artificial intelligence

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

Cite this

Zhou, J., Yu, L., Mabu, S., Hirasawa, K., Furuzuki, T., & Markon, S. (2007). Double-deck elevator systems using genetic network programming with reinforcement learning. In 2007 IEEE Congress on Evolutionary Computation, CEC 2007 (pp. 2025-2031). [4424722] https://doi.org/10.1109/CEC.2007.4424722

Double-deck elevator systems using genetic network programming with reinforcement learning. / Zhou, Jin; Yu, Lu; Mabu, Shingo; Hirasawa, Kotaro; Furuzuki, Takayuki; Markon, Sandor.

2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. p. 2025-2031 4424722.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhou, J, Yu, L, Mabu, S, Hirasawa, K, Furuzuki, T & Markon, S 2007, Double-deck elevator systems using genetic network programming with reinforcement learning. in 2007 IEEE Congress on Evolutionary Computation, CEC 2007., 4424722, pp. 2025-2031, 2007 IEEE Congress on Evolutionary Computation, CEC 2007, 07/9/25. https://doi.org/10.1109/CEC.2007.4424722
Zhou J, Yu L, Mabu S, Hirasawa K, Furuzuki T, Markon S. Double-deck elevator systems using genetic network programming with reinforcement learning. In 2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. p. 2025-2031. 4424722 https://doi.org/10.1109/CEC.2007.4424722
Zhou, Jin ; Yu, Lu ; Mabu, Shingo ; Hirasawa, Kotaro ; Furuzuki, Takayuki ; Markon, Sandor. / Double-deck elevator systems using genetic network programming with reinforcement learning. 2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. pp. 2025-2031
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