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A Review of Research on the Development of Elevator Group Control Technology

Zewen Li1,*, Kemei He2

1Guangdong Institute of Special Equipment Inspection and Research Huizhou Institute, Huizhou, Guangdong, China.

2Guangzhou Banknote Printing Co., Ltd., Guangzhou, Guangdong, China.

*Corresponding author: Zewen Li

Published: November 30,2023


This paper introduces the growing demand for elevator traffic in the urbanization process in our country. It emphasizes the need for multiple elevators to be installed to meet this demand. If the elevators are unable to work in synchronization, it can result in multiple elevators responding to a single call signal. After analyzing the cause of the problem, we establish a mathematical model and use the objective function of the algorithm to optimize elevator group control. The main feature of this system is that it utilizes current elevator group control technology to collect statistical data on passenger flow and elevator usage frequency within the elevator group. Based on this data, a combination of fuzzy control optimization, expert system optimization, neural network optimization, and genetic algorithm optimization is selected to achieve efficient scheduling, tailored to the specific needs of the situation. The practical application of this technology demonstrates its ability to effectively address the issues of single response signal in traditional elevators, long waiting times, and significant energy loss. The technical model is more accurate, but the algorithm is complex and the solution takes a long time. Additionally, the timeliness problems affecting elevator group control technology will need to be further improved by introducing cloud computing in the future.


David S. Modelling elevator traffic with social distancing in a university classroom building [J]. Building Services Engineering Research & Technology, 2021, 42 (1): 82-97.

Hitoshi Aoki. Group supervisory control system assisted by artificial intelligence [J]. Elevator World. 1990, 47(5): 70-80.

Jikang X, Ruichuan L, Yanchao L, et al. Research on Variable-Universe Fuzzy Control Technology of an Electro-Hydraulic Hitch System [J]. Processes, 2021, 9 (11): 1920-1920.

Minglei F, Qi Z, Kezhen R, et al. Integrated dynamic multi-threshold pattern recognition with graph attention long short-term neural memory network for water distribution network losses prediction: An automated expert system [J]. Engineering Applications of Artificial Intelligence, 2024, 127 (PB).

Seong K.A., Chang Bum Kim. A fuzzy approach to elevator group control system [J]. IEEE transactions on systems, man, and cybernetics, 1995, 25(6):985.

Yang C, Kim H. Memristor Bridge Synapse-based Neural Network Circuit Design and Simulation of the Hardware-Implemented Artificial Neuron [J]. Journal of Institute of Control, Robotics and Systems, 2015, 21 (5): 477-481.

Zhang G M, Gong J M, Shan-Tung T U. Elevator Group Control System Using Multi-agent Based on GA-RL Algorithm [J]. Journal of East China University of Science and Technology (Natural Science Edition), 2009, 35(04):606-611.

How to cite this paper

A Review of Research on the Development of Elevator Group Control Technology

How to cite this paper: Zewen Li, Kemei He. (2023). A Review of Research on the Development of Elevator Group Control TechnologyEngineering Advances3(5), 438-442.