Research On Modeling And Optimal Allocation For Resource In Cloud Manufacturing

The rapid development of network information technology has driven the global manufacturing industry to transform and upgrade in the direction of intellectualization,servitization and customization.In recent years,the research of advanced manufacturing model to promote the deep integration of network technology and the manufacturing industry has become a hotspot research for academic and industrial.Cloud manufacturing has emerged as a service-oriented networked manufacturing model.Cloud manufacturing is the application and development of cloud computing in the manufacturing industry,which embodies the idea of "scattered resources are used centrally" and "centralized resources are served scattered".Through the centralized management and operation of distributed manufacturing resources,it realizes the efficient sharing and rational utilization of manufacturing resources,and provides users with readily available,on-demand,high-quality and inexpensive manufacturing services.The core idea of cloud manufacturing is "manufacturing as a service",which encapsulates manufacturing resources and manufacturing capabilities into cloud services through virtualization and servitization methods,and adopts key technologies such as service matching and service optimization selection to realizes efficient sharing and collaborative manufacturing of manufacturing resources among enterprises.Based on the existing research at home and abroad,this thesis investigates the service encapsulation,matching and selection,and dynamic adjustment of manufacturing resources based on the heterogeneous and distributed characteristics of manufacturing resources,the complexity and diversity characteristics of service demands,the dynamics and stability characteristics of cloud manufacturing platform in the cloud manufacturing model.The main research contents of this thesis is described as follows:(1)The architecture of cloud manufacturing for SMEs is studied.By analyzing the typical features and the operation mode of cloud manufacturing for SMEs,the process and functional requirements of registration,acquisition and management of cloud service in the cloud manufacturing service platform are proposed.On this basis,the functional structure of the cloud manufacturing platform based on multi-agent is constructed,and the types of agents and their interaction with each other are analyzed.The cloud manufacturing key technology system is established based on the analysis of the implementation technology of each functional module.(2)A semantic-based manufacturing resource virtualization modeling and serviceoriented encapsulation method is proposed for the characteristics of diversity,heterogeneity and complexity of manufacturing resources in cloud manufacturing.First,according to the commonality of manufacturing resources and the demand of serviceoriented encapsulation,a cloud manufacturing resource formal description model is constructed to abstractly describe manufacturing resources as manufacturing capabilities.Then,ontology modeling technique is used to construct a semantic-based cloud service structure model to solve the semantic heterogeneity problem in the process of manufacturing resource description by normalizing the description of manufacturing resource-related concepts,properties,axioms,and rules.Finally,the cloud service structure model is transformed into a cloud service description model through the manufacturing resource instantiation method to realize the service-based encapsulation of manufacturing resources,and a VMC-2100 B vertical machining center is used as an example to describe the service-based encapsulation and registration method of manufacturing resources.(3)A semantic-based manufacturing cloud service matching and combination method is proposed for the matching problem of service demand and cloud service in cloud manufacturing.For the matching requirement of single cloud service,a semanticbased manufacturing cloud service search and matching method is proposed,which calculates the semantic similarity between service requirements and cloud services based on the cloud service structure model;for the matching requirement of combined cloud services,a task requirement decomposition and combined service matching method based on task relevance is proposed.First,Hierarchical Task Network(HTN)is applied to decompose the task requirements into a set of atomic tasks with constrained relationships.Then,according to the task unit design principle,a task reorganization method based on task relevance is proposed to reorganize the atomic tasks into a set of task units,and a semantic-based manufacturing cloud service search and matching method is used in the reorganization process to match each task unit with a candidate cloud service that meets the functional requirements,which effectively solves the problem of disconnect between task requirement decomposition and combined cloud service matching process.Finally,the effectiveness of the proposed cloud service matching and combination method is verified by taking the task requirements of a shaft part machining as a case study.(4)Aiming at addressing several conflicting criteria of quality of service(Qo S)that should be trade-off optimized during the service composition and optimal selection(SCOS)in cloud manufacturing the improved non-dominated sorting genetic algorithm III(NSGA-III)is proposed and employed to address the SCOS issue.This is the first time that a preference-based multi-objective algorithm has been used to address the SCOS problem.A K-level preference reference point generation method is proposed to improve the reference point generation strategy of the original algorithm,and accordingly,a new fitness assignment strategy and an environment selection scheme is proposed to integrate user preference information into the algorithm execution process through reference points and guide the search towards the interesting parts of the Pareto optimal region based on user preferences in order to increase the selection pressure of the population individuals and improve the efficiency and convergence of the algorithm.Additionally,the memetic algorithm is integrated into the evolutionary mechanism of the algorithm to address the insufficiency of local search.To validate the performance of the proposed algorithm,several test cases are conducted.The results demonstrate that the proposed algorithm can search a set of cloud service composition optimization configuration schemes with good convergence and diversity according to user preference weight.(5)To address the dynamic adjustment of abnormal cloud service nodes in cloud manufacturing,a multi-agent based framework for handling abnormal cloud manufacturing services is proposed,and a cloud service abnormality adaptive adjustment model is designed for task agents.Based on the above model,a cloud service anomaly adaptive adjustment algorithm is proposed.First,the objective function based on stability and Qo S indexes is established according to the characteristics of cloud manufacturing environment.Then,the Artificial Bee Colony Algorithm(ABC)is improved by using the honey source generation strategy,honey source neighborhood search strategy and honey source adaptation value calculation strategy,and the improved algorithm is applied to the solution of the above objective function.Finally,the effectiveness of the proposed method is verified by simulation experiments,and the experimental results show that the proposed method can efficiently realize the dynamic adjustment of the abnormal cloud service and maintain the stability of the cloud manufacturing platform.