Dynamic Optimal Matching Method And Application For Machine Tool Resource In Cloud Manufaturing Environment

Cloud Manufacturing(CMfg)provides traditional manufacturing enterprises an effective transformation approach towards the networked,informatized and serviceoriented manufacturing paradigm.Machine tool resource(MTR)is one of the critical manufacturing resources in CMfg and improving the reliability and stability of MTR cloud service is significant to the implementation of CMfg.How to provide the reliable and stable MTR cloud service with complex and changeable MTR demands and service capability status dynamic changing MTR has become a critical issue for CMfg.Hence,based on the research results at home and abroad,this paper studies the MTR dynamic optimal matching method in CMfg.Firstly,the random disturbance factors during the CMfg task execution period are analyzed.The structures of MTR demand and MTR are studied and a general idea for MTR dynamic optimal matching considering the impact of random disturbances is designed.Secondly,from the perspective of the resource demander,the detailed categories of MTR demand changes and their corresponding MTR dynamic optimal matching scopes are analyzed.Based on the Markov Decision Processes,an MTR dynamic optimal matching requirement model after demand changes is constructed and the Cross-Entropy method is applied to obtain the MTR optimal matching results after MTR demand changes.Thirdly,from the perspective of the resource supplier,the MTR dynamic optimal matching requirements after MTR status changes are analyzed.Based on the discrete Markov Jump System(MJS),a CMfg quality of service(Qo S)evolution model is established considering the status changes of MTR.Combining with the discrete MJS stability control theory,an MTR dynamic optimal matching strategy consisting of CMfg task minimal Qo S verification and Qo S stability control is designed,which implements the MTR dynamic optimal matching considering MTR service capability changes.Finally,the above research is preliminarily tested and validated based on the research group's previous research achievement-Cloud Manufacturing Service Platform for Machine Tool.