Research On Large-scale Flexible Job Shop Schedule Method Based On Job-oriented And Operation-oriented Decomposition

In the context of globalized production,market demand changes rapidly,market competition also increases,whether or not manufacturing companies can respond quickly and accurately to market demands determines the market share of companies,therefore companies need to continuously improve production capacity,product quality,and reduce production cost to improve its own competitiveness.The schedule is an indispensable part of the manufacturing system and directly affects the economic efficiency and competitiveness of the company.Based on the comprehensive analysis of existing large-scale flexible job shop schedule methods and strategies at home and abroad combined with the actual production situation of the company,large-scale flexible job shop schedule methods were proposes in this paper.The class schedule problem is solved by improved algorithm quickly and effectively,and a large-scale flexible job shop schedule prototype system is developed,and finally verified the method proposed in this paper through examples.The results show that the solution accuracy and efficiency can be significantly improved.The main contents are as follows:(1)A large-scale flexible job shop schedule model job-oriented decomposition and improved genetic algorithm are proposed.According to the characteristics of the workpiece and other features,a machine-workpiece relationship matrix is established to determine the batch method and the schedule model is constructed.Using an improved genetic algorithm to optimize the solution,an approximate optimal schedule scheme is obtained.It is verified by the example that the method reduces the time of completion of the workpiece and simultaneously improves the efficiency of the optimizing schedule scheme,therefore the flexible job shop schedule problem which is with many kinds of workpieces,multiple quantities,multiple processes and small batches can be solved effectively;(2)A large-scale flexible job shop schedule model operation-oriented decomposition and an improved leapfrog genetic algorithm are proposed.On the basis of group batch schedule method,the large-scale flexible job shop schedule problem based on operation-oriented decomposition is studied.The sub-problem decomposition strategy,which satisfies the established process sequence constraints of the workpiece,is proposed,and the schedule model is established.In the improved leapfrog genetic algorithm,some rules are utilized to generate part of the initial population individuals to improve the initial population quality,the idea of chaotic random numbers is applied to generate dynamically the frog update step size,the improved local renewal strategy of the frog leaping algorithm is put use to improve the genetic crossover operator;and finally,MATLAB is applied to achieve parallelly solving sub-problems.The large-scale flexible job shop schedule problem is decomposed into several small and medium-sized schedule sub-problems by the schedule model,which reduces the space for solution and brings down the difficulty of solution.A satisfactory solution can be obtained by the improved algorithm within a reasonable time;(3)Development of a large-scale flexible job shop schedule prototype system.The prototype system is developed with Microsoft Visual Studio2010 software development platform as a development tool and the C# language as a development language.The system integrates resource management,production task management and production schedule,providing a convenient and quick management tool for production managers.Finally,examples are given to verify the proposed large-scale flexible job shop schedule methods,the results show that the solution accuracy and efficiency can be greatly improved.