Production management of hybrid flow shop based on genetic algorithm

Abstract

The production management of hybrid flow shop (HFS) has a great practical significance. Proper production management can improve the machine utilization and shorten the makespan in a complex production control environment. However, the relevant research has not paid enough attention to realistic constraints like multi-period control, and job transport time. To solve the problem, this paper explores the production management of HFS based on improved genetic algorithm (GA). Specifically, several assumptions were proposed for the multi-objective optimization problem of HFS production management, and new constraints like multi-period control, and job transport time were introduced to the problem. Then, the authors established a multi-objective optimization model for HFS production management, and improved the traditional GA to solve the model more rapidly and accurately. The proposed model and algorithm were proved effective through experiments.