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.
Highlights
With the development of control engineering technology, many enterprises have gradually raised the awareness of industrial intelligent automation
Rail guide vehicle (RGV) problems have the characteristics of fast running, stable performance, and high automation
The RGV has the characteristics of fast running, stable performance and high automation, the dynamic scheduling problem associated with RGV can effectively improve the production efficiency of modern intelligent processing, which has been widely used in various workshops and automated warehouses
Summary
With the development of control engineering technology, many enterprises have gradually raised the awareness of industrial intelligent automation. Rail guide vehicle dynamic scheduling problem is an important branch in the automation industry. The RGV is mainly applied in production scheduling workshop, logistics transportation, component assembly, and many other fields. The RGV has the characteristics of fast running, stable performance and high automation, the dynamic scheduling problem associated with RGV can effectively improve the production efficiency of modern intelligent processing, which has been widely used in various workshops and automated warehouses. The RGV can be divided into self-driven type, passive-driven type, assembly type and transport type according to the driving mode and the purpose (Martina et al, 2018; Sáez et al, 2008).
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