Research On The Robotic Manufacturing Cell Of Job-shop Type Based On Improved Particle Swarm Optimization

Due to the fierce competition in the global economy and increasingly scarce resources,optimization of production process for manufacturing industry is more and more important.Increasing attention has been paid in intelligent manufacturing.As industrial robots are widely employed in manufacturing production,under small scale and specific customized requirements,the traditional Flow-Shop scheduling can be inadequate.The Job-Shop scheduling mode with robotic manufacturing cell can better meet the needs of the current manufacturing enterprises,therefore,many related research have been conducted.This paper studied the Job-Shop scheduling problem with single robotic manufacturing cell on the basis of the classical theory of shop scheduling.Variable Neighborhood Search based Discrete Particle Swarm Optimization(PSO)algorithm was proposed.This algorithm and an exact method solution(under CPLEX)were used to solve the problem.The results proved its reliability and effectively.The main contents of this paper are as follows:First,the robotic manufacturing cell is exhibited with an example,and then the type of production system,including system units,equipment layout,the scheduling constraints,operation status were described and analyzed.The optimization goal of this kind of problem in this part was summarized.After,the paper analyzes the type of Job-Shop robotic manufacturing cell with a single handling robot and the mathematical model through the actual test cases.The CPELX was used to solve the problem.By comparing the benchmark test case,the reliability of the method is validated.Finally,aiming at the problem,a novel approach is proposed based on Variable Neighborhood Search based Discrete Particle Swarm Optimization(PSO).The encoding of the algorithm and the way of field search were designed.Compared with the original algorithms,the improved algorithm has shown better performance.