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Dynamic Scheduling Model of Rail-Guided Vehicle (RGV) Based on Genetic Algorithms in the Context of Mobile Computing

Dynamic Scheduling Model of Rail-Guided Vehicle (RGV) Based on Genetic Algorithms in the Context of Mobile Computing

Chen Xu, Xueyan Xiong, Qianyi Du, Shudong Liu, Yipeng Li, Deliang Zhong, Liu Yaqi
Copyright: © 2021 |Volume: 12 |Issue: 1 |Pages: 20
ISSN: 1937-9412|EISSN: 1937-9404|EISBN13: 9781799860082|DOI: 10.4018/IJMCMC.2021010103
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MLA

Xu, Chen, et al. "Dynamic Scheduling Model of Rail-Guided Vehicle (RGV) Based on Genetic Algorithms in the Context of Mobile Computing." IJMCMC vol.12, no.1 2021: pp.43-62. http://doi.org/10.4018/IJMCMC.2021010103

APA

Xu, C., Xiong, X., Du, Q., Liu, S., Li, Y., Zhong, D., & Yaqi, L. (2021). Dynamic Scheduling Model of Rail-Guided Vehicle (RGV) Based on Genetic Algorithms in the Context of Mobile Computing. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 12(1), 43-62. http://doi.org/10.4018/IJMCMC.2021010103

Chicago

Xu, Chen, et al. "Dynamic Scheduling Model of Rail-Guided Vehicle (RGV) Based on Genetic Algorithms in the Context of Mobile Computing," International Journal of Mobile Computing and Multimedia Communications (IJMCMC) 12, no.1: 43-62. http://doi.org/10.4018/IJMCMC.2021010103

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Abstract

Track guidance vehicle (RGV) is widely used in logistics warehousing and intelligent workshop, and its scheduling effectiveness will directly affect the production and operation efficiency of enterprises. In practical operation, central information system often lacks flexibility and timeliness. By contrast, mobile computing can balance the central information system and the distributed processing system, so that useful, accurate, and timely information can be provided to RGV. In order to optimize the RGV scheduling problem in uncertain environment, a genetic algorithm scheduling rule (GAM) using greedy algorithm as the genetic screening criterion is proposed in this paper. In the experiment, RGV scheduling of two-step processing in an intelligent workshop is selected as the research object. The experimental results show that the GAM model can carry out real-time dynamic programming, and the optimization efficiency is remarkable before a certain threshold.

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