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ISSN: 2637-6679

Research and Reviews on Healthcare: Open Access Journal

Research Article(ISSN: 2637-6679)

Epidemic Prediction on Bionical Intelligent RGV

Volume 5 - Issue 2

Jinming Cao1, Bin Zhao2* and Mingzhe E2

  • Author Information Open or Close
    • 1School of Information and Mathematics, Yangtze University, Jingzhou, China
    • 2School of Science, Hubei University of Technology, China

    *Corresponding author: Bin Zhao, School of Science, Hubei University of Technology, Wuhan, Hubei, China

Received: March 19, 2020;   Published: May 15, 2020

DOI: 10.32474/RRHOAJ.2020.05.000209

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This paper studies the dynamic scheduling problem of bionical intelligent RGV, Modeling and Simulation of RGV scheduling problem in 4 cases of bionical intelligent machining system, and the simulation results of the model are tested by using three sets of real working data of RGV. Under known conditions, according to the working condition of a process, the RGV scheduling path is taken as the decision variable, and as much as possible clinker is processed as the objective function. Based on the idea of 0-1 planning, the RGV dynamic scheduling model is established when a process does not occur. Then a heuristic algorithm is constructed for the modeled model. Through simulation, the total production of materials under three sets of parameters is 383, 358, 392, and the system operation efficiency is not less than 98%. For the random failure of the processing system, the random failure probability is increased to 1% on the existing model, and the manual troubleshooting is performed. The duration is subject to the uniform distribution of 10~20 minutes, and the RGV dynamic scheduling model for single and double process machining under the condition of CNC probability failure is established. The total production of materials under one process is 357, 336, 362 respectively. The efficiency is as high as 98.3%, 95.9% and 98.4%; the total production of materials under the two processes is 223, 336, 362 respectively. Industry efficiency were 95.7%, 96.7% and 96.2%. Data simulation results show that the dynamic scheduling model for the establishment of RGV and practical algorithm.

Keywords: Multi-objective optimization; Bionical intelligent RGV; 0-1 planning; Optimal scheduling; Simulation

Abstract| Introduction| Related Work| Problem Hypothesis| Model Establishment| Model Solution| Conclusion| Conflict of Interest| Acknowledgement| References|


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