Research On Optimization Of Hybrid Flow Shop Scheduling With Missing Operations

Production scheduling is the core of modern management technology and advanced manufacturing technology.It is an important way for enterprises to improve productivity,reduce production costs and enhance comprehensive strength.The hybrid flow shop scheduling problem with missing operations is a new type of scheduling problem.It is similar to the hybrid flow shop scheduling problem but is essentially different.It is widely used in industries such as manufacturing and steel industry,and has a strong industrial application background.This paper considers the transfer times and the setup times of jobs during the production process and establishes single-objective and multi-objective optimization model for the hybrid flow shop scheduling problem with missing operations.Then an improved genetic algorithm is designed to solve the above model.Finally,using the simulation software Plant Simulation,the optimized scheduling scheme is simulated.The main contents of this paper are as follows:(1)The research background and significance of this paper are expounded.And the domestic and international research status of the hybrid flow shop scheduling problem and the hybrid flow shop scheduling problem with missing operations are introduced.Then the shortcomings of existing research literature are summarized.(2)This paper summarizes the hybrid flow shop scheduling problem with missing operations and its characteristics and performance indicators.And the impact of the missing operations on scheduling is analyzed.Then the basic principles of genetic algorithm and iterated local search algorithm are introduced.(3)This paper establishes a single-objective optimization model for hybrid flow shop scheduling problem with missing operations to minimize the maximum completion time.The model considers the impact of the transfer times and the setup times of jobs on scheduling.In order to solve the model,an improved genetic algorithm is designed by combining genetic algorithm and iterated local search algorithm.Then the effectiveness of the method is verified by Matlab simulation of actual cases.Finally,the effects of different processing methods of the transfer times and the setup times on single-objective scheduling results are analyzed.(4)Based on the single-objective optimization model,a multi-objective optimization model for the hybrid flow shop scheduling problem with missing operations to minimize the maximum completion time,the total machine idle time,and the total machine energy is established.The improved genetic algorithm is used to solve the model,and then the effectiveness of the method is verified by Matlab simulation of actual cases.Finally,the effects of different processing methods of the transfer times and the setup times on multi-objective scheduling results are analyzed.(5)The production line model was established using Plant Simulation,and the model was used to simulate different scheduling schemes(scheduled schemes optimized by standard genetic algorithm,NSGA-II and improved genetic algorithm).Then the machine utilization,the machine idle time,the residence time of jobs in the buffer area and the total flow time of jobs are analyzed and compared.