云制造环境下的智能服务单元具有自适应学习能力,并通过社会关系与其他服务进行资源传递、信息共享交互,协作完成复杂制造项目。根据云平台上制造任务关联性与服务协同性的新特征,提出考虑学习与协同效应的制造任务双边匹配决策方法。由于云交易的重复性、动态性,服务通过多次参与任务积累知识提升服务质量,构造基于学习效应模型的主体动态能力计算方法,运用期望效用理论聚合双方满意度。同时,应用协同网络刻画服务社会关系,基于社会网络理论计算服务间协同满意度。从而构建以任务、服务满意度、服务间协同满意度最大化的一对一双边匹配多目标模型。通过汽车云制造实例运算得到最优匹配方案,验证本文模型的有效性,并与一般双向匹配、考虑学习、考协同效应的3类模型比较,证明本文模型的优势,更符合实际制造场景要求。
In the cloud manufacturing environment, smart service units with adaptive learning capacity can conduct resource delivery, information sharing and interaction with other services through social relationships to complete complex manufacturing projects together. According to the new features of manufacturing task relevance and service collaboration, a two-sided matching decision method for manufacturing tasks considering learning and synergy effects is proposed on the cloud platform. Due to the dynamics and repeatability of cloud transactions, services involved in multiple tasks task can accumulate experience and knowledge to improve its service quality. A calculation method of dynamic capability is put forward based on learning effect model, and mutual satisfaction for task and service can be aggregated by applying expected utility theory. Meanwhile, a synergy network is used to describe service social relationships, and collaboration satisfaction among services can be measured through synergy effect based on social network theory. Thus, taking the tasks', services' and inter-service collaborative satisfaction maximization as objective, a one to one two-sided matching muti-objective model is constructed considering the influence of learning and synergy ability on task execution performance. Through automobile cloud manufacturing case study, the optimal matching scheme is obtained to verify the validity of our model. Comparing with other three types of matching model, the proposed model has been obviously proved to be better in line with the requirements of actual manufacturing scene.
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