A Study of Dynamic Scheduling Strategy of Intelligent Machining System

Abstract

Rail Guided Vehicle (RGV) is used in the field of automated logistics system and automated warehouse. It has the characteristics of high speed, high reliability and low cost. The combination of RGV and Computer Number Controller (CNC) can greatly improve production efficiency. This paper designs an optimal dynamic scheduling strategy and a shortest walking path of RGV for an intelligent machining system composed of one RGV and eight CNC. Firstly, based on the shortest path algorithm and the principle of "nearest priority", this paper designs three optimal RGV dynamic scheduling models to adapt the three different cases. In these models, the optimization objective is the total number of clinkers produced by eight CNC, the decision variable is the number of clinkers produced by each CNC, and the constraints are determined by the time when each CNC issues instructions. Secondly, according to the different properties of the three models, the corresponding solutions have been given. Meanwhile, in order to describe the RGV scheduling strategy and the operational efficiency of the system, this paper uses the cost-time and average time spent for generating a clinker to measure the operational efficiency of the system. If their ratio were closer to 1, the work efficiency is higher. If the ratio were closer to 0, the work efficiency is lower. Finally, in order to test the practicability and validity about the three models, we import the known data into the three models to find how to manage the CNC and when to carry out the downloading and uploading operations by MATLAB simulation. The practicability of the optimized dynamic scheduling model and the corresponding calculation are verified.