Research On Manufacturing Capacity Matching And Collaborative Plan Making Under Cloud Manufacturing Mode

Cloud manufacturing is a smart manufacturing model and method in the Internet era.In the cloud manufacturing platform,manufacturing resources and manufacturing capabilities are formed into a shared "pool",which is uniformly allocated by the cloud platform to realize on-demand usage.The sharing and circulation of manufacturing resources and manufacturing capabilities are the foundation of cloud manufacturing platform operations,and a series of intelligent decision-making tools are required to manage and allocate manufacturing resources and manufacturing capabilities.In practice,due to the property and diversity of manufacturing resources,they cannot be used directly through the network.Therefore,cloud platform's management and allocation of "manufacturing capacity" reflected in manufacturing activities of manufacturing resources is more accord with the actual demand.Using two-side matching theory and social network theory,this thesis focuses on the modeling,matching and scheduling of manufacturing capabilities,the key points are providing models,methods and algorithms to support the effective allocation of manufacturing capabilities in cloud manufacturing mode.The main research contents of this thesis are as follows:(1)Aiming at the description and measurement of manufacturing capability in cloud manufacturing mode,the components of manufacturing capacity are analyzed,and the formal description and measurement of manufacturing capacity are proposed.According to the components of manufacturing capacity,the formal description of manufacturing resources,manufacturing activities and manufacturing capacity is given,three kinds of manufacturing capability measurement models are formulated based on activity organization,manufacturing cycle and objective expectation,and a case is used to explain the application of the measurement methods.(2)Aiming at the two-sided matching problem between manufacturing capability and manufacturing task based on hybrid preference information in cloud manufacturing platform,a two-sided matching method considering stability and satisfaction is proposed.Facing the situation that manufacturing task commonly use multi-index information while manufacturing capability commonly used ordered interval preference information,the multi-index evaluation information is transformed into ordered interval preference information,and the one to many matching problem between manufacturing capability and manufacturing task is transformed into corresponding one-to-one matching problem.A related definition of one-to-one stability with weight constraints in the order interval is proposed,and a two-sided stable satisfactory matching model with the goal of maximizing satisfaction between both parties is constructed.Two linear inequality constraints are designed to ensure the stability of the matching,and the ideal point method is adopted to solve the model.In order to verify the effectiveness of the method,the two-sided stable satisfactory matching,two-sided satisfactory matching and two-sided stable matching are used to solve the same example,it is proved that the matching result obtained by the proposed method is stable and satisfactory matching result.(3)Aiming at the matching problem between the manufacturing capability and manufacturing task with multiple sub tasks under the cloud manufacturing platform,a two-sided matching method considering stability and satisfaction is proposed.By dividing the relationship between manufacturing capabilities in cloud platform manufacturing network,the correlation strength is transformed into corresponding satisfaction degree,and a multi-objective model with the maximum satisfaction degree of manufacturing capability provider and sub task publisher and the maximum satisfaction between manufacturing capability providers,stable matching is taken as a constraint condition in the model,and the NSGAII algorithm is used to solve the model.The calculation example verifies that the matching result obtained by the proposed method is stable and satisfactory.(4)Aiming at the problem of collaborative production with limited manufacturing capacity of various branches in the enterprise under the cloud manufacturing platform,a collaborative production planning model considering order decomposition and production time windows was constructed,and an improved adaptive simulated annealing genetic algorithm is designed to solve the problem and the solution result was compared with CPLEX,it proves that the algorithm designed in this thesis has better optimization ability and strong robustness.The relationship between capacity utilization and key variables in the model are also discussed and which provide decision reference for manufacturing capacity configuration.In summary,this thesis studies the connotation,formal description and measurement model of manufacturing capability,enriching the description and measurement methods of manufacturing capability under the cloud manufacturing model;Applies two-sided matching theory and social network theory to the research of manufacturing capacity matching,and provides new ideas for the research of manufacturing capacity matching;studies multi-plants collaborative planning considering manufacturing capacity constraints and provides decision reference for manufacturing capacity configuration under cloud manufacturing platform.