Dynamic Scheduling Strategy of Intelligent RGV Based on Multi-layer Predictive Optimization

Yunhui Zeng, Yilin Chen, Hongfei Guo*, Li Huang and Wenjuan Hu
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Yunhui Zeng: College of Intelligent Science and Engineering, Jinan University, No. 206, Qianshan Road, Xiangzhou District, Zhuhai City, Guangdong Province, China
Yilin Chen: College of Intelligent Science and Engineering, Jinan University, No. 206, Qianshan Road, Xiangzhou District, Zhuhai City, Guangdong Province, China
Hongfei Guo*: College of Internet of Things and Logistics Engineering, Jinan University, No. 206, Qianshan Road, Xiangzhou District, Zhuhai City, Guangdong Province, China
Li Huang: College of Intelligent Science and Engineering, Jinan University, No. 206, Qianshan Road, Xiangzhou District, Zhuhai City, Guangdong Province, China
Wenjuan Hu: College of Intelligent Science and Engineering, Jinan University, No. 206, Qianshan Road, Xiangzhou District, Zhuhai City, Guangdong Province, China

Academic Journal of Applied Mathematical Sciences, 2019, vol. 5, issue 2, 7-13

Abstract: This paper takes the dynamic scheduling of intelligent RGV as the research object and explores the problem of materiel machining of intelligent RGV for one and two procedures. In the process of establishing a materiel machining operation model for one procedure, firstly, the banker algorithm is used to provide a scheduling strategy for the RGV, dynamically predict the evolution process of the resource allocation process, and determine the order in which the CNC performs the task. Then, the non-preemptive least laxity first concept is introduced to improve the utilization rate of CNC and minimize the time for the computer machine tools CNC to wait for response. In order to simplify the calculation and improve the feasibility, based on the main idea of the banker algorithm, the evolution of the situation is only carried out in three levels, which makes the algorithm calculate moderately and has certain reference value for the prediction of the evolution process. Moreover, in the process of establishing a materiel machining operation model for two procedures, the bat algorithm is used to establish the model from the macroscopic perspective, and finally the dynamic scheduling strategy of RGV is obtained. In this paper, the dynamic scheduling strategy of intelligent RGV established for the materiel machining for one and two procedures provides a theoretical basis for the development of RGV dynamic scheduling strategy in the actual production process.

Keywords: Multi-layer predictive optimization; RGV dynamic scheduling; Banker algorithm; The least laxity; Bat algorithm. (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:arp:ajoams:2019:p:7-13

DOI: 10.32861/ajams.52.7.13

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