Application Research of Clustering and NSGA-Ⅱ Joint Algorithm in Hybrid Flow Shop
In order to improve the scheduling difficulty of mixed-flow production in the final assembly shop of a high-end equipment manufacturing enterprise,the difficulty of product group batch in the batch processing stage and realize the joint optimi-zation of multi-objective in the workshop,this paper investigates the multi-objective optimization problem of hybrid flow shop with batch processors.Firstly,a multi-objective optimization model is established according to the operation situation of the workshop,and then a joint method based on K-means clustering algorithm and non-dominated sorting Genetic Algorithm ⅱ(NSGA-Ⅱ)is proposed.A clustering process is designed to group incompatible products,and a double-layer coding method based on product group number and product number within the group is designed.A complete group batch process is designed for batch operations.Fi-nally,the workshop production case is used to test,and the results are compared with the results obtained by only using NSGA-Ⅱ,which verifies the effectiveness of the proposed method.
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