Please use this identifier to cite or link to this item:
A Comparative Study on the Coordinated Operations of Multi-elevators.
|關鍵字:||電梯群控系統;Elevator Group Control System;基因演算法;電梯停駐法則;genetic algorithm;elevator stop rules||出版社:||電機工程學系所||引用:|| Zhangyong Hu, Yaowu Liu , Qiang Su, and Jiazhen Huo, “A multi-objective genetic algorithm designed for energy saving of the elevator system with complete information,” IEEE International Energy Conference and Exhibition (EnergyCon), pp.126-130, Dec. 2010. Jixian Meng, Xinzhong Lu, GongYao Wu, and Yandong Gao, “The application of genetic algorithms in high-rise elevator systems,” Seventh International Conference on Natural Computation (ICNC), pp.1126-1130, July 2011.  K. Hirasawa, T.Eguchi, Jin Zhou, Lu Yu, Jinglu 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: Applications And Reviews, vol. 38, no. 4, July 2008.  T.Ishikawa, A.Miyauchi, and M.Kaneko, “Supervisory Control for Elevator Group by Using Fuzzy Expert System which also Addresses Traveling Time,” IEEE International Conference on Industrial Technology, pp. 87-94, Jan. 2000.  Yasuyuki Sogawa, Tomo Ishikawa, and Kazuyuki Igarashi, “Supervisory Control for Elevator Group by Using Fuzzy Expert System which address the Riding Time,” IEEE IECON 22nd International Conference on Industrial Electronics, Control, and Instrumentation, pp. 419-424, Aug. 1996.,  Atsuya Fujino, Toshimitsu Tobita, and Kazuhiro Segawa et al., “An Elevator Group Control System with Floor-Attribute Control Method and system Optimization Using Genetic Algorithms,” IEEE Transactions on Industrial Electronics, vol. 44, issue 4, pp. 546-552, Aug 1997.  T. Tobita, A. Fujino, K. Segawa, K.Yoneda, and Y. Ichikawa, “A parameter tuning method using genetic algorithm for an elevator group system ,” IEEE IECON 22nd International Conference on Industrial Electronics, Control, and Instrumentation, vol. 2,pp 823-828, Aug. 1996. Jin Sun, Qian-Chuan Zhao, and Peter B. Luh, “Optimization of Group Elevator Scheduling With Advance Information,” IEEE Transactions on Automation Science and Engineering, vol. 7, issue 2, pp. 352-363, April 2010. Jung-Hwan Kim, Byung-Ro Moon, “Adaptive elevator group control with cameras,” IEEE Transactions on Industrial Electronics, vol. 48, issue 2, pp. 377-382, April 2001.  Jianzhe Tai, Suying Yang, Hong Tan,“Dispatching approach optimization of elevator group control system with destination floor guidance using Fuzzy Neural Network,” Proceedings of the 7th World Congress on Intelligent Control and Automation, pp.8702-8705, June 2008. Yine Zhang, Yun Yi ,“The Application of the Fuzzy Neural Network Control in Elevator Intelligent Scheduling Simulation,” International Symposium on Information Science and Engineering, Dec. 2010. Zhifeng Pan, Fei Luo, Yuge Xu,“Elevator Traffic Flow Model Based On Dynamic Passenger Distribution,” IEEE International Conference on Control and Automation, pp. 2386-2390, 2007. Jun Wang, Airong Yu, Xiaoyi Zhang, and Lei Qu,“Research of Dispatching Method in Elevator Group Control System Based on Traffic Mode Identify,” International Conference on Business Intelligence and Financial Engineering, pp.46-49, July 2009.  Holland, J.H., Adaptation in Natural and Artificial Systems, Ann Arbor, MI: The University of Michigan Press, 1975.  Holland, J.H., “Genetic Algorithm,” Sci. Am., pp.66-72, July 1992. 楊聖智，“運用基因演算法於控制電梯群體系統，”國立台灣師範大學資訊教育研究所，民國九十一年八月。 周鵬程，“遺傳演算法原理與應用-活用Matlab，”全華圖書股份有限公司，民國九十四年。 Tsung-Che Chiang, Li-Chen Fu,“ Design of Modern Elevator Group Control Systems,” IEEE International Conference on Robotics and Automation, pp.1465-1470, May 2002.||摘要:||
第二章敘述電梯群控制系統的概念，且對Hall Call、Car Call、電梯狀態等作定義。接著，介紹基因演算法的原理與流程。最後，提出一些影響電梯派車的因素，例如:距離、方向、擁擠度等進行討論與分析。
第四章將進行實驗模擬與分析，驗證本研究所提出的演算法。首先，為了更真實的模擬電梯環境，本研究使用Flash CS5 軟體以動畫的方式來實現電梯模擬系統，並且在尖峰、離峰時段對各個演算法作分析與比較。
With a greater number of building floors, elevator service efficiency has become increasingly important in modern buildings. Coordinating the operation of multiple elevators to reduce passengers’ waiting time and riding time during peak and peak-off hours is a significant research topic for academia and the industry. This study a shortest-path algorithm and a genetic algorithm with various elevator stop rules to analyze traffic flow.
Chapter 1 introduces the research motivation and the basic structure of elevator group control system. One of the study researchers used an expert system, genetic algorithm, and neural network to enhance the elevator service efficiency.
Chapter 2 presents a description on the concept of the elevator group control system and defines the hall call, car call, and elevator status. We then explain the principles and processes of genetic algorithms. Finally, we propose factors that may affect elevator dispatching for discussion and analysis.
Chapter 3 introduces a discussion on the shortest-path algorithms and genetic algorithms we applied to the elevator group control system. We subsequently assessed the various elevator stop rules developed using different algorithms.
As explained in Chapter 4, we conducted an experimental analysis and simulation to validate the algorithms employed in this study. To simulate the elevator environment realistically, we used Flash CS5 software to create the elevator system. We then compared and analyzed the results of the various algorithms on different traffic flow patterns.
Finally, Chapter 5 provides a summary of the research and suggestions to improve the experimental results.
|Appears in Collections:||電機工程學系所|
Show full item record
TAIR Related Article
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.