Modeling Simulation And Optimization Of Scheduling System In Machine Tool Parts Processing Workshop

Nowadays,the requirements of intelligent manufacturing plants for CNC machine tools are increasingly demanding.These plants not only require higher machining accuracy of CNC machine tools,but also need to optimize production scheduling system to improve production efficiency and reduce production cost.With the increasing production of machine tool parts,the job shop scheduling model,which was originally built only by experience,is far from meeting the needs of mass production of the present manufacturing enterprises.Therefore,business managers urgently need to develop a good job shop scheduling plan for production logistics system to meet the needs of today's business development.In order to solve the job shop scheduling problem of the logistic system for some related parts in high-end CNC machine tools such as lathe bed,column,spindle box,sliding saddle,work table and so on,a logistics scheduling problem is presented in this paper.The scheduling system of machine parts processing workshop is transformed into the displacement flow shop scheduling problem for modeling and Simulation.The g-MOEAD algorithm and QUEST software are used to analyze and optimize the production logistics scheduling of machine tool parts processing workshop,and to solve the production problems in the machine parts processing workshop.Aiming at the replacement flow shop scheduling problem of machine tool parts processing workshop of a company,the multi-objective optimization scheduling model of machine parts processing workshop is set up,three functions including the maximum completion time,maximum delay time and inventory cost are constructed,and these three functions are minimized as the objectives,and a multi-objective optimization model is establishedIn this paper,the improved MOEA/D algorithm is applied to solve the above problems.The g dominance theory is introduced into the MOEA/D algorithm.Combined with the two-norm weight vector,the MOEA/D algorithm(g-MOEA/D algorithm)of g dominance strategy is proposed,g dominance combines the traditional Pareto frontier and the usage of reference points to make the decomposition based multi-objective evolutionary algorithm generate the set of effective solutions that adapt to the preference of the decision-makers,instead of the whole Pareto solution set or the single effective solution,thus improving the optimal effect and convergence of the MOEA/D.10 different scale displacement flow shop scheduling tests are used to verify the comparison between the g-MOEA/D and the original MOEA/D algorithm.The experiment shows that the g-dominace can be effectively combined with the MOEA/D algorithm to get a better multi-objective optimization solution and a verification result with satisfactory performance index.The algorithm is further applied to practical problems.QUEST software is used for modeling and Simulation of machine parts processing workshop.In order to more directly reflect the optimization effect of g-MOEA/D algorithm for processing workshop data.CATIA software is used to build 3D physical models of machine parts which are imported into QUEST,and then QUEST software is used to build physical modeling and logical link of the production line of machine tool parts processing workshop.Finally,two satisfactory solutions are selected from the two algorithms respectively.The optimization results of g-MOEA/D algorithm,MOEA/D algorithm and the original scheduling scheme are input to the QUEST model for simulation.The simulation data are compared and analyzed through the parameters such as the utilization rate of the QUEST simulation output equipment and the idle time of the equipment,as to prove the feasibility of the g-MOEA/D algorithm.Through the algorithm optimization and software simulation analysis of the replacement flow shop scheduling problem of the machine parts processing workshop,the production cost of the machine parts processing workshop is effectively reduced and the production efficiency of the workshop is improved.