Research On The Optimization Method Of Two-way Matching Of Processing Services In Cloud Manufacturing Environment

Cloud manufacturing is one of the key directions in the current research of domestic and international manufacturing informatization.As a new service-oriented networked manufacturing mode,cloud manufacturing uses cloud computing,Internet of Things,big data and other fast-developing information technologies to centrally manage and allocate idle manufacturing resources.The cloud-manufacturing platform brings together a large number of manufacturing resources with different characteristics.At the same time,the personalized needs of users are constantly improving.In this complex environment,how to choose the right services from a variety of service resources for specific manufacturing tasks has become a focus and a hotspot of current cloud manufacturing research.Through detailed research on cloud manufacturing concepts,service optimization strategies,resource description modeling,and service composition algorithms in current cloud manufacturing research,this paper aims to improve the profit of service providers and to improve the profit balance of cloud manufacturing services.The main research contents include:1)A new service-matching model(bidirectional matching mode)and its framework are proposed.Based on the characteristics of this proposed mode,the game theory is used to establish a strategy.The bidirectional matching mode solves the problem of uneven income of the two parties in the unidirectional matching mode to some extent.2)Combining the characteristics of machining services,the optimization information models of both parties are proposed,which provides a basis of mathematical modeling for the service combination optimization involving personalized service requirements.At the same time,the ontology semantic method is used to establish an information relationship model between the two parties.The protege is used to generate the relevant file code,which lays a foundation for the cloud manufacturing service virtualization process.3)Based on the basic idea of bidirectional matching mode,the service optimization framework for implementing the optimization matching process is established.The evaluation index model of the two parties in the bidirectional mode and the objective function model of the service parties under various service combination schemes are proposed.In addition,based on the artificial bee colony algorithm,an improved optimization algorithm,named bidirectional game artificial bee colony(BGABC)algorithm,for solving the optimization problem in this mode is proposed.The BGABC better overcomes the problem that the artificial bee colony algorithm converges slowly and easily leads to local optimization.Moreover,by adding a crossover strategy and refining equilibrium condition,the algorithm can obtain the optimization goal in the bidirectional matching mode.4)Three types of simulation cases were designed.By comparison with the Genetic algorithm and particle swarm optimization algorithm in the same conditions,the feasibility of the proposed BGABC algorithm is verified.By comparing the changes in the revenue of the two parties in the unidirectional mode and the bidirectional mode,the superiority of the bidirectional mode in achieving the goal of this paper is verified and analyzed.5)Finally,based on the develpment results of the project team-A turning processing cloud service platform,the main service flow of the prototype system was demonstrated,and a preliminary experimental verification of the above research contents was carried out.In the process of service discovery and matching,current research is more about pursuing the interests of service demanders,while ignoring the interests of service providers.This paper provides a new solution to the problem of service optimization configuration in cloud manufacturing by studying the optimized matching mode,service model and algorithm.