Application Of Improved Genetic Algorithm In Multi-objective Flexible Job Shop Scheduling

An excellent scheduling solution can save the resources consumed by the enterprise in the production process,compress the various production costs required for production inwardly,and establish a good brand image of the enterprise outwardly,which can enhance the ability of the enterprise to face the market and competitors.Job Shop Scheduling Problem is a problem model obtained by simplifying the actual production job shop by ignoring some factors,and the solution of this problem model has positive significance for the formulation of scheduling plan in actual production.With the development of intelligent algorithm theory,algorithms have been used to obtain good performance in solving the model of the problem,so it is of good theoretical and practical meanings to get excellent and widely applicable algorithms.This paper is based on the genetic algorithm and solves the flexible job shop scheduling problem with single and multiple optimisation objectives in the case of single and multiple optimisation objectives by improving its operation and mixing it with the Improved Empire Competition algorithm,respectively.As following is the major work of the study:(1)Based on the characteristics and definitions of the research problem,we determine the constraints in the research problem and the selected optimization objective function,construct the problem model after specifying the factors considered and ignored by the model,build the algorithm optimization platform by programming language,and verify the solution performance of the algorithm with benchmark cases.(2)After verifying the performance of the improved genetic algorithm in solving the single-objective problem model through the example,a simple control group is set up to try to obtain better parameter settings,and the data results of each control group are analyzed to select more suitable parameter values.(3)After the initial improvement of the genetic algorithm,in order to solve the high-dimensional examples,the improved imperial competition algorithm is introduced on the basis of the original algorithm for pipelined mixing,and the performance of the proposed algorithm in solving single-objective and multi-objective problem models is verified,by comparing the results with those of other algorithms,after solving the multi-objective problem model using several cases,and the optimal solutions’ Gantt chart of is given.