Study For The Permutation Flow Shop Scheduling Of M Enterprise Based On Improved Genetic Algorithm

Scientific production line layout and production scheduling methods are extremely necessary for modern manufacturing enterprises.Aiming at the the multi-process,multi-variety,small batch production process of M enterprises,this paper has carried out the production line transformation,Permutation flow shop scheduling problem modeling,optimization and application research.The main research work of the thesis is as follows:Firstly,a new production line layout method was designed for M companies,and data collection methods and production scheduling optimization schemes were developed around the new production line.The key production line is selected and abstracted into permutation flow shop scheduling problem to study.Genetic algorithm has the advantages of fast speed and strong global search ability.It is suitable for solving complex problems with large scale.Therefore,this paper takes GA as the basic algorithm and makes further improvements to optimize the design.Secondly,M company's new U-shaped production line needs to train multi-functional workers,and the preparation time and processing time can b e considered separately.For the new U-shaped production line of M enterprise,multi-functional workers need to be cultivated.The preparation time can be separated from the processing time.With the minimum completion time as the optimization goal,a PFSP scheduling model with setup time is established.Later,an adaptive genetic simulated annealing algorithm was designed.GA is used to solve the PFSP with setup time,and Metropolis sampling strategy in simulated annealing is introduced to enhance the global search ability of GA.Introduce adaptive operator to adjust crossover and mutation probability to avoid premature convergence of iteration results.Finally,the experimental examples are solved,and the results are compared with the algorithms in other literatures.The results show that the algorithm is effective in solving PFSP with setup time.Thirdly,aiming at the optimization goal of slewing bearing enterprises considering energy saving and ensuring productivity,a PFSP multi-objective scheduling model considering energy consumption and setup time was constructed.A multiple objective adaptive genetic simulated annealing algorithm based on Pareto was designed.Roulette were used to choose Pareto to decentralize individuals in areas with large crowding degree and store them in external archives,so that the non-dominated front has better dispersion.According to the difference in stand-by energy consumption,the evolutionary probability and evolution direction of individuals in the population are calculated,and the traditional LOX crossover method was extended to non-equilength gene fragments to adapt to the crossover operation determined by the difference in stand-by energy consumption.At the same time,combined with the Metropolis sampling strategy,which has a better global search capability.Finally,through the example test of M enterprise,the effectiveness of the algorithm is verified.Finally,for the PFSP scheduling model that is expected to minimize the maximum completion time and consider the setup time,using MATLAB,SQL Server,C#,the M enterprise production scheduling optimization system V1.0 was developed.