Traffic sequence optimization method for trailer automated guided vehicle system

被引：0
作者：
Xiao, Haining [1 ]
Wang, Min [1 ]
Peng, Minghua [2 ]
Zhang, Biao [1 ]
Zhao, Bin [1 ]
Sun, Huihui [1 ]
机构：
[1] College of Mechanical Engineering, Yanchcng Institute of Technology, Yancheng
[2] School of Management, Xi'an University of Science and Technology, Xi'an
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2024年 / 30卷 / 12期
基金：
中国国家自然科学基金;
关键词：
neighborhood search; traffic sequence optimization; trailer automated guided vehicle system; whale optimization algorithm;
D O I：
10.13196/j.cims.2023.0265
中图分类号：
学科分类号：
摘要：
To address the shortcomings of Automated Guided Vehicle System(AGVS) that cannot optimize intersection traffic sequences and increase the risk of traffic congestion and task delay, a trailer AGVS traffic sequence optimization method based on an Improved Multi-Objective Whale Optimization Algorithm (IMWOA) was proposed. According to the distribution process of vehicle assembly accessories and the operational characteristics of trailer AGV, a mathematical model for optimizing the traffic sequence of AGV at intersections was established with the comprehensive optimization objectives of maximizing the remaining time of the most urgent task and minimizing the number of blocked paths. To overcome the shortcomings of slow convergence speed and weak global optimization ability of traditional whale optimization algorithms, a neighborhood search strategy was adopted to accelerate the convergence speed of the algorithm, and a non-dominated sorting mechanism with elite retention strategy was adopted to maintain population diversity and enhance the algorithm s global optimization ability. An IMWOA suitable for solving the traffic sequence optimization mathematical model was designed. Finally, a trailer AGVS traffic management simulation analysis platform was developed to validate the proposed method. Simulation results showed that the proposed 1WOA had better global optimization ability by comparing with algorithms such as NSGA- II» tabu search and traditional whale optimization algorithm, and could achieve higher assembly line productivity with lower task execution time. © 2024 CIMS. All rights reserved.
引用
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页码：4406 / 4419
页数：13
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