Research On Flexible Job Shop Scheduling Problem Based On Improved Genetic Algorithm

With the impact of economic globalization,China's manufacturing industry has developed rapidly,it has begun to slowly transform and is moving towards manufacturing power,resulting in China's manufacturing industry is facing important challenges.If the manufacturing industry wants to survive,it needs to meet the customers' requirements of product diversification,generalization,intelligentization,customization and personalization.The core technology of job shop scheduling is an important factor affecting the healthy development of enterprises.In order to meet the needs of the economic development and to adapt to the market quickly,flexible manufacturing system has become the need of enterprises.Flexible job shop scheduling problem exists in all kinds of flexible manufacturing systems.This paper studies the scheduling problem of flexible job shop,and the work is as follows:Aiming at the problem of uncertain factors in the actual processing process,fuzzy number is used to represent inaccurate machining information.An improved genetic algorithm is proposed to solve the problem of Fuzzy Flexible Job Shop Scheduling,the optimization objective of the research problem is to minimize the maximum completion time,the uncertain processing time in fuzzy flexible job shop scheduling problem is represented by triangular fuzzy number.This algorithm aims at the complexity of flexible job shop scheduling problem,a double-layer coding method based on process sequencing and process corresponding machine number is designed,the fitness value is calculated according to the fuzzy operation,a new transformation formula is used to transform the objective function value into fitness value,in the selection operation,roulette method was used to select excellent individuals from the population to enter the next generation population,in the crossover operation,two different crossover methods were used to select excellent individuals.Finally,the feasibility of the model and the effectiveness of the algorithm are verified by MATLAB simulation and experimental comparison.Aiming at the problems of flexibility of production process and diversification of management decision-making,multi-objective flexible job shop scheduling has become a research hotspot.The paper takes the maximum completion time,the delay time,the total load of the machine and the total energy consumption as the optimization objective,an improved NSGA-? is proposed to solve the multi-objective flexible job shop scheduling problem,the coding and decoding,Pareto sorting,selection strategy and crossover mutation operation of the algorithm are studied.The double-layer individual coding mode of process sequencing and machine selection is adopted;in the process of elite selection,by calculating the slope of the individual,the small slope can be entered into the parent generation,so that the excellent individual can be preserved;in the mutation process,based on the key process of the neighborhood block structure,the insertion method is used to make the small operation work pieces be processed first,so that the maximum completion time is significantly reduced.Finally,the feasibility of the model and the superiority of the algorithm are verified by MATLAB simulation and experimental comparison.