Research On The Integrated Scheduling Method Of Flexible Job Shop Machines And AGVs

Under the background of intelligent development of production and manufacturing,more and more manufacturing enterprises use Automated Guided Vehicle(AGV)instead of manual material handling in the production process,which promotes the production process to the direction of integration.At the same time,the production mode of the workshop has changed from single-machine Scheduling and assembly line processing to multi-variety and small-batch production mode,and the Flexible Job Shop Scheduling Problem(FJSP)has become the research focus of the workshop scheduling problem in recent years.Therefore,the overall planning of the production process and distribution process of the flexible job shop is of great significance to realize the intelligent scheduling of the shop.The integrated scheduling problem of flexible job shop machine and AGV is studied in this paper.The main contents are as follows:First,the mathematical model of FJSP is established by taking the most commonly used maximum completion time in production activities as the objective function.Aiming at the shortcomings of traditional genetic algorithm which is easy to fall into local optimization and slow convergence,two neighborhood structures based on critical path are designed to improve the initial population,decoding strategy and crossover strategy,and an improved genetic algorithm is proposed.The simulation results of Kacem and Brandimatre examples show the effectiveness of the proposed strategy,and the efficiency of the proposed algorithm is verified by comparing with other algorithms in literature.Secondly,on the basis of FJSP,aiming at the integrated scheduling problem of flexible job shop machine and AGV,a mathematical model of the common constraints of shop process route,AGV resources and machine was established to minimize the maximum completion time.A population initialization method based on machine load balancing is designed considering the coding structure and objective function.An AGV scheduling strategy based on improving the utilization rate of AGV is proposed.The simulation results of a benchmark example verify the high efficiency of the proposed strategy.By studying the effect of the number of AGVs on the completion time,it is found that the number of AGVs in the workshop conforms to the law of diminishing marginal utility,so as to provide reference for the allocation of AGVs in the workshop.Finally,the integrated scheduling problem of multi-objective flexible job shop machine and AGV is studied,and the effects of shop energy consumption,such as processing energy consumption,replacement energy consumption,transportation energy consumption,no-load energy consumption and auxiliary energy consumption are analyzed to minimize the completion time and total energy consumption,and a multi-objective mathematical model is established.An improved NSGA-II algorithm is designed,and the population is stratified by fast branching sorting,Pareto solution set is determined,and child population is formed by elite retention strategy.Based on machine load ratio,local search algorithms for different machine limit load balancing and different machine limit local load balancing are designed.Finally,a test example is given to verify the effectiveness of the algorithm,and the algorithm is used to solve the production scheduling problem of the piston manufacturing shop,and the Pareto solution set is obtained,and the solution set is decided by the grey relational degree analysis method,and a better scheduling scheme is selected.The research work in this thesis has excellent performance in solving the flexible job shop scheduling problem,and provides a reference for the actual job shop scheduling.