The Research And Application Of Improved Adaptive Non-dominated Sorting Genetic Algorithm In Scheduling Of Shutter Factory

In manufacturing production,flexible job shop scheduling problem is one of the core contents of enterprise production management,and it is also a difficult problem to solve.It is very important to obtain scientific and efficient scheduling scheme for enterprise production management and production efficiency.As a kind of flexible job shop,louver shop scheduling problem is also one of the most difficult NP problems.This paper takes this kind of workshop as an example to seek more optimized and effective solution methods and means.In the louver shop scheduling environment,for solving multi-objective problems,various constraints must be considered,and each objective may conflict with each other.The first task is to solve the conflict and get a relatively optimal solution.In recent years,many scholars have proposed a variety of optimization algorithms to solve the multi-objective job shop scheduling problem.The traditional multi-objective optimization algorithm has many defects.Non dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)is a fast non dominated multi-objective optimization algorithm with elite retention strategy.It is a multi-objective algorithm based on Pareto optimal solution.Due to its high efficiency and good diversity of solutions,NSGA-Ⅱ is widely used in solving multi-objective problems,but it also has some disadvantages.In this paper,an adaptive algorithm is introduced to change the crossover and mutation probabilities to improve the original NSGA-Ⅱ.In the initial population stage,the heuristic algorithm is introduced,and the weight aggregation method is used to constrain the total completion time and carbon emissions;the elite strategy is improved by using simulated annealing method to replace the son with the parent to improve the quality of the replacement population.The innovative parts are as follows: firstly,the heuristic algorithm is introduced in the initial population stage,and the weight aggregation method is used to constrain the total completion time and carbon emissions.Secondly,the elite strategy is improved,and the simulated annealing method is used to replace the son with the father to improve the quality of the replacement population.The improved non dominated sorting genetic algorithm with elitist strategy can obtain Pareto optimal solution set faster,and adaptively change the crossover and mutation probability according to the diversity of population,and effectively improve the diversity of population.The algorithm in this paper is tested by standard test set,and the convergence speed and diversity of the algorithm are improved to a certain extent.On the basis of considering the machine load,the maximum completion time is minimized.When two machines with different carbon emissions in the same processing time are processed,the machine with low carbon emission will be selected.Finally,in order to prove the practical significance of the algorithm,the improved adaptive non dominated sorting genetic algorithm(IANSGA-Ⅱ)is applied to the actual production workshop of a louver factory,and the workshop scheduling system is developed,and the better practical application effect is achieved.