Research On Flexible Job Shop Scheduling Considering Transportation Time

In order to meet the individual needs of the people,the production of personalized products,gradually establish a new variety,small batch production mode.In the production process,if the enterprise uses the automatic guide car to transport the job,the research on the transport and charging problems of the automatic guide car will have more theoretical significance,and the research results will be better applied to guide the actual production.Firstly,this paper introduces the research background of the subject and the excellent research achievements of domestic and foreign articles.Then,on the basis of the flexible job shop model,the maximum completion time as the optimization goal,describe the basic principle of genetic algorithm,a genetic algorithm for solving the flexible job shop scheduling problem with shipping time,through the three standard calculate column simulation,compared with transport time and without shipping time of two models of dispatching target,according to the results,The Gantt chart obtained by optimizing flexible job shop scheduling problems considering transportation time is more helpful to guide actual production.Later,establish multiple homing car transportation mathematical model of the flexible job shop,consider homing car charge constraint problem,the car within the fixed time interval charging method is adopted,to embed the car charging process chromosome decoding process,using hybrid genetic simulated annealing algorithm to solve the problem of the model.Through the simulation of the standard example,the influence of the number of AUV on the completion time is analyzed.The results show that when the number of AUV increases from small to large,the maximum completion time decreases gradually.When the number of AUV reaches a certain value,the influence of the change of the number of AUV on the maximum completion time is weakened.Analysis about the transport time of the job is transfer completion time,the influence of the simulation results show that,with the adjacent machine tools and machining average average transport time and time ratio increases,compare the two models problem the improvement effect of numerical first increasing after decreasing trend,numerical appear increasing influence the problem is the cause of the main contradictions have not changed,The reason for the decline is that the transportation time of the job becomes the main contradiction affecting the maximum completion time.Finally,the AGV active decoding strategy is established based on the idea of activity scheduling from the perspective of optimizing the transportation process of the automatic guided vehicle.The strategy is applied to the transportation optimization of the key job When there is a single no-load AGV that can transport the key job and make the key job arrive at the machine tool earlier,the original AGV gene is replaced.The hybrid genetic algorithm is used to solve the problem,and several standard examples are simulated to compare the results of the solution with the AGV active decoding strategy and without the AGV active decoding strategy.The results show that the solution with the AGV active decoding strategy can get a smaller maximum completion time.