Optimal strategy for intelligent rail guided vehicle dynamic scheduling. (October 2020)

• Record Type: Journal Article
• Authors: Ding, Chao; He, Hailang; Wang, Weiwei; Yang, Wanting; Zheng, Yuanyuan
• Is Part Of: Computers & electrical engineering. Volume 87(2020)
• Publication Date: 2020-10
• Subjects: Foresight stepping model -- Chaotic particle swarm -- GBDT algorithm -- BP network algorithm -- Intelligent RGV; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106750
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: The proposed method improves the production efficiency of the machine. The reliability and superiority of the proposed method are verified by simulation. The proposed method in this paper is able to achieve the optimal solution. Abstract: 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. Graphical abstract: Image, graphical abstract The problem addressed in this paper is the work optimization problem in a workshop.