Research On Flow Shop Scheduling Problem Based On Improved CIA

Production planning and scheduling plays an important role in enterprise production.The traditional CMS is divided into five levels:the enterprise decision-making layer, management layer, production planning and scheduling, monitoring layer, control layer, and production planning and scheduling is at the middle, so it not only passes the enterprise management control instruction to control layer, but collects monitor layer monitoring information and timely feedback to the management, in order let management to adjust measures in time.Therefore, production planning and scheduling in enterprise production is very important.In this paper, to solve the Flow Shop scheduling problem, we design and improve the intelligent optimization algorithm, and the simulation results demonstrate the feasibility and validity of the algorithm.For the traditional Flow Shop scheduling problem, we introduced an improved cooperative immune algorithm.This algorithm adopts the co-evolutionary algorithm and immune algorithm, we use immune algorithm for global search ability and cooperative evolutionary algorithm effectively to shorten the search path.In view of the traditional cooperative immune algorithm’s later search ability problem, we design a new population selection mechanism-"80/20rule", according to the algorithm initial convergence speed, we join local search algorithm.Simulation results show that, the improved cooperative immune algorithm is better and more efficient than the basic cooperative immune algorithm and genetic algorithm.For a lot of processing enterprises, especially the chemical processing industry, the traditional Flow Shop scheduling can not meet the actual production requirements.In these industries, the intermediate product must be transported into the next processing machine immediately, which requires zero wait Flow Shop model.In this pater,the improved cooperative immune algorithm is introduced into this kind of problem, through a large number of simulation experiments and comparation of the traditional cooperative immune algorithm and genetic algorithm, the simulation results verify the efficiency of the improved cooperative immune algorithm; for Makespan scheduling objective with zero wait Flow Shop scheduling problem, based ont the improved cooperative immune algorithm, we introduced global crossing.The experiment proves that this new intersection method has a very good effect in the preservation of excellent gene fragment and increasing the diversity of population.The simulation experiment use standard numerical example to verify.and the comparation with genetic algorithms s cooperative immune algorithm and improved cooperative immune algorithm proves the superiority of this method.