Research And Application Of Flexible Manufacturing Cell Production Scheduling Optimization

Intelligent manufacturing is the foothold for the development of modern industry in China and the world,and is a "weapon" for improving the manufacturing level of manufacturing enterprises.The flexible manufacturing unit is widely used to solve the multi-variety,small-batch production mode due to its high flexibility,automation,and strong production capacity.It has gradually become an indispensable part of building intelligent factories.For flexible manufacturing cells,an efficient scheduling solution is the key to improving workshop production efficiency andimproving customer satisfaction with products,and is also the basis for achieving intelligent manufacturing.This paper studies the production scheduling problem of a flexible manufacturing unit in a domestic enterprise.By analyzing the characteristics of the manufacturing unit,a transport robot between various processing equipment during the processing process is considered.A production scheduling model for dynamic events is designed,and a hybrid optimization algorithm is designed to solve the model.Then apply the designed scheduling scheme to the enterprise's manufacturing execution system to help the enterprise achieve "smart scheduling" and increase productivity.In the design of hybrid optimization algorithm,the genetic algorithm is used as the main frame,and the characteristics of the mountain climbing algorithm are added in the initialization process of the algorithm,and the "hill climbing operation" is performed on half of the population to improve the quality of the initial solution.The selection method combined with roulette,in order to increase the probability of good genes being inherited,to facilitate the evolution of the population in a better direction;in the cross operation,the idea of particle swarm algorithm is incorporated,and a kind of chromosome selected by the machine is used.The crossover method is guided by the current optimal individual of the population to improve the crossover operator to expand the search ability of the algorithm.Finally,the validity of the optimization algorithm is verified by a benchmark example of a flexible job shop and the movement schedule of the transport robot between processing equipment given in the article.