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Fitness Distance Correlation Strategy for Solving the RGV Dynamic Scheduling Problem

Fitness Distance Correlation Strategy for Solving the RGV Dynamic Scheduling Problem

Wei Li, Furong Tian, Ke Li
Copyright: © 2020 |Volume: 14 |Issue: 3 |Pages: 21
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799805335|DOI: 10.4018/IJCINI.2020070102
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MLA

Li, Wei, et al. "Fitness Distance Correlation Strategy for Solving the RGV Dynamic Scheduling Problem." IJCINI vol.14, no.3 2020: pp.20-40. http://doi.org/10.4018/IJCINI.2020070102

APA

Li, W., Tian, F., & Li, K. (2020). Fitness Distance Correlation Strategy for Solving the RGV Dynamic Scheduling Problem. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 14(3), 20-40. http://doi.org/10.4018/IJCINI.2020070102

Chicago

Li, Wei, Furong Tian, and Ke Li. "Fitness Distance Correlation Strategy for Solving the RGV Dynamic Scheduling Problem," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 14, no.3: 20-40. http://doi.org/10.4018/IJCINI.2020070102

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Abstract

Rail guide vehicle (RGV) problems have the characteristics of fast running, stable performance, and high automation. RGV dynamic scheduling has a great impact on the working efficiency of an entire automated warehouse. However, the relative intelligent optimization research of different workshop components for RGV dynamic scheduling problems are insufficient scheduling in the previous works. They appear idle when waiting, resulting in reduced operating efficiency during operation. This article proposes a new distance landscape strategy for the RGV dynamic scheduling problems. In order to solve the RGV dynamic scheduling problem more effectively, experiments are conducted based on the type of computer numerical controller (CNC) with two different procedures programming model in solving the RGV dynamic scheduling problems. The experiment results reveal that this new distance landscape strategy can provide promising results and solves the considered RGV dynamic scheduling problem effectively.