Optimal strategy for intelligent rail guided vehicle dynamic scheduling

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.

[1]  A.J.R.M. Gademann,et al.  An order batching algorithm for wave picking in a parallel-aisle warehouse , 1996 .

[2]  H. Ishibuchi,et al.  Multi-objective genetic algorithm and its applications to flowshop scheduling , 1996 .

[3]  Lu Zhi-jun.  Kinematics Simulation and Design Improvement of Rail Guide Vehicle(RGV)Based on ADAMS , 2012 .

[4]  Bo Liu,et al.  Improved particle swarm optimization combined with chaos , 2005 .

[5]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[6]  Allan Borodin,et al.  Adversarial queuing theory , 2001, JACM.

[7]  M. Sakawa,et al.  An efficient genetic algorithm for job-shop scheduling problems with fuzzy processing time and fuzzy duedate , 1999 .

[8]  J. Detemple,et al.  A Monte Carlo Method for Optimal Portfolios , 2000 .

[9]  Jiawei Liu,et al.  Research and Analysis of Intelligent RGV Based on Dynamic Scheduling Optimization Model , 2019 .

[10]  Hani S. Mahmassani,et al.  Hybrid route generation heuristic algorithm for the design of transit networks , 1995 .

[11]  Cristián E. Cortés,et al.  Hybrid adaptive predictive control for the multi-vehicle dynamic pick-up and delivery problem based on genetic algorithms and fuzzy clustering , 2008, Comput. Oper. Res..

[12]  Charles J. Malmborg,et al.  A network queuing approach for evaluation of performance measures in autonomous vehicle storage and retrieval systems , 2009, Eur. J. Oper. Res..

[13]  L. Li.  China's manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0” , 2017, Technological Forecasting and Social Change.