基于加权协同网络的制造服务组合方法

Manufacturing Service Composition Method Based on Service Weighted Synergy Network

为解决复杂制造业务过程中的广泛存在的资源运输、信息传递和知识共享问题,保障任务的高效执行,将协同能力作为服务竞争力评估和选择的重要指标,提出基于加权协同网络的制造服务组合方法。根据云平台上服务交互合作大数据分析,运用社会关系强度计算衡量协同效应以构建加权协同网络。以最大化整体Qo S值和协同效应为目标,建立制造服务选择多目标优化模型。通过双向学习、最优种群更新和群体交互速度更新机制,构造改进的引力搜索算法(Gravitational search algorithm,GSA)对模型求解。通过智能汽车云制造仿真试验,验证了模型和算法的有效性,得到符合实际制造场景的最优服务方案。

In order to solve the problems that resources translation, information handoff and knowledge sharing widely exist in the complex manufacturing business process and ensure the efficient task implementation, a manufacturing service composition method based on weighted synergy network is proposed, where collaboration capacity is a key index for service competence evaluation and selection. Based on big data analysis of service interaction and cooperation in cloud platform, synergy effect can be measured and obtained through the weighted aggregation of social relationship strength, and then service weighted synergy network is naturally constructed. Hence, a muti-objective optimization model of manufacturing service selection for highest overall Qo S and synergy effect is put forward. Applying two-way learning, population update and group interaction based speed update mechanism, a improved GSA algorithm is built to solve the model. Through the simulation experiment of intelligent automobile cloud manufacturing, the validity of our model and algorithm is verified, and the optimal service scheme in line with the actual manufacturing situation is obtained finally.