Research On Cloud Manufacturing Service Combination Optimization Based On Harmoney Search Algorithm

Cloud manufacturing is a new kind of manufacturing model which is service-oriented,networked and intelligent.It mergers some technologies together such as cloud computing,big data,Internet of Things and high performance computing technology and carries through virtualization encapsulation on manufacturing resources and manufacturing capacity,so as to form cloud manufacturing services and make them merger together to form a resource pool of cloud manufacturing services.The cloud manufacturing system combines and calls cloud manufacturing services resources according to the needs which are submitted by users in the cloud manufacturing platform.Therefore,manufacturing services resources can be responded and scheduled more efficiently and can also be configured in a more reasonable way.However,in the process,how to better realize the optimization of cloud manufacturing services combination is the key problem that needs to be further researched.The cloud manufacturing model reorganizes the loosely distributed manufacturing service resources and builds a manufacturing resource pool that is managed and scheduled by the cloud manufacturing platform by integrating large-scale,diversified,discrete manufacturing resources and capabilities.The cloud manufacturing service portfolio is an orderly combination of cloud services according to certain rules.Each tightly-coordinated manufacturing service chain is formed for manufacturing tasks.The appropriate method is used to optimize the composite service so that the combined manufacturing cloud service meets customer satisfaction in terms of manufacturing time,cost,and quality,etc.Among them,the process of portfolio service optimization has become an NP-hard problem due to the large number of selectable manufacturing services and manufacturing service portfolio paths.The optimization problem of cloud manufacturing services combination is a typical NP-hard problem,which is nonlinear,multiple-targeted and uncertain that makes the problem face many challenges when the model is established.This paper modifies the time calculation method of the model in the existing literature,first discriminates the service with the largest service execution time in the parallel service,and then counts it into the total service execution time.The modified model was solved by the harmony search algorithm and the results of different parameters were compared and analyzed.The results show that the combinationof the cloud manufacturing service sequence obtained by adding the user's expected model can reach a higher level.The user expects the degree of completion.At the same time,the solution process shows that the harmony search algorithm has great potential for solving cloud manufacturing service composition optimization problems.Finally,this paper analyzes the same type of multi-task service composition process in cloud manufacturing,and constructs the same multi-task service optimization model of cloud manufacturing that considers the average degree of completion of multi-task users,and uses the harmony search algorithm to solve the examples.At the same time,the combination of cloud manufacturing service sequences that allows users of multiple tasks to expect a high degree of completion indicates that the model built can reflect the expectations of multiple users to a certain extent,so that the resulting service sequence combination is closer to the user's needs.It has enriched the relevant research on cloud manufacturing multi-task service composition optimization issues and has certain reference value for further research in the future.