Optimal strategy for intelligent rail guided vehicle dynamic scheduling

被引：4
|
作者：
Ding, Chao [1 ,2 ]
He, Hailang [1 ]
Wang, Weiwei [1 ]
Yang, Wanting [1 ]
Zheng, Yuanyuan [1 ]
机构：
[1] Anhui Jianzhu Univ, Coll Environm & Energy Engn, Hefei 230601, Anhui, Peoples R China
[2] Univ Sci & Technol China, State Key Lab Fire Sci, Hefei 230026, Anhui, Peoples R China
来源：
COMPUTERS & ELECTRICAL ENGINEERING | 2020年 / 87卷
基金：
中国国家自然科学基金;
关键词：
Foresight stepping model; Chaotic particle swarm; GBDT algorithm; BP network algorithm; Intelligent RGV; GENETIC ALGORITHM;
D O I：
10.1016/j.compeleceng.2020.106750
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
In an automated stereoscopic warehouse, the efficiency of the Rail Guided Vehicle (RGV) is the bottleneck. This paper proposes a foresight stepping model to optimize the intelligent RGV scheduling scheme. We incorporate the chaotic particle swarm optimization algorithm into the model and design the mechanism of multi-step processing. The machine optimization is used to compare the optimal alignment effect of the Back Propagation (BP) network algorithm and GradientBoostingDecisionTree (GBDT) algorithm. The real-life system test is performed by simulation. The simulation results show that the GBDT-foresight stepping model is superior to the traditional models in terms of complexity, reliability and accuracy. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数：14
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