Based on Intelligent RGV Dynamic Scheduling Model of Particle Swarm Optimization

2018 4th International Conference on Environmental Science and Material Application

生态环境科学;材料科学

Jia, Yunlong^1

Shandong Jianzhu University, China^1

关键词: Dynamic scheduling methods;    Dynamic scheduling problems;    Intelligent processing;    Manufacturing enterprise;    Numerical experiments;    Optimization techniques;    Production efficiency;    Research and application;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/252/5/052135/pdf
DOI  :  10.1088/1755-1315/252/5/052135

来源: IOP

【 摘 要 】

The dynamic scheduling of intelligent RGV is an important factor that affecting the production efficiency of intelligent processing systems, and it plays an important role in manufacturing enterprises to improve production efficiency. This paper analyzes the dynamic scheduling problem of intelligent RGV by establishing a reasonable RGV dynamic scheduling model. First of all, it starts from the case where the machine does not malfunction, and the processing of one process and two processes is expressed by a 0-1 integer plan respectively. Secondly, the shortest time to start processing is the objective function. The single-process RGV static scheduling model and the dual-process RGV static scheduling model based on 0-1 integer plan are established respectively, and the model is solved by particle swarm optimization. On the basis of the RGV static scheduling model, the machine failure condition of the CNC is regarded as the state of continuous operation of the CNC by the case of the machine failure. This paper passes the original model in the case of possible machine failure. The constraint conditions are added, and the single-process RGV dynamic scheduling model and the dual-process RGV dynamic scheduling model are established. Finally, the practicality and effectiveness of the built model and algorithm are verified by numerical experiments, and the simulation experiments of the two models are carried out using eM-Plant software. The models and algorithms established in this paper are effective research and application of dynamic scheduling methods and optimization techniques, which play an important role in manufacturing enterprises to improve production efficiency and reduce costs.