To cope with Multi-objective Programming on Manufacturing Cloud Service Composition ( MOP-MCSC) problem in cloud manufacturing (CMfg) system, a mathematical model and a solution algorithm were proposed and studied. Firstly, inspired by the resource service composition technology in manufacturing grid, a QoS-aware MOP-MCSC model in CMfg system had been explored and described. Secondly, by analyzing the characteristics of manufacturing cloud services according to the domain knowledge of manufacturing, an eight-dimensional QoS evaluation criterion with corresponding quantitative calculation formulas was defined. Then, the QoS expression of manufacturing cloud service was eventually formulated. Lastly, the MOP-MCSC model was built, and an Adaptive Mutation Particle Swarm Optimization (AMPSO) was designed to realize this model. The simulation experimental results suggest that the proposed algorithm could solve the MOP-MCSC problem efficiently and effectively with a better performance than conventional particle swarm optimization.%针对云制造系统中制造云服务组合的多目标规划问题,研究建立了问题模型并提出了求解方法.首先引入了网格制造模式的制造资源服务组合技术,探讨并描述了云制造模式中基于服务质量(QoS)的制造云服务组合过程；接着通过分析云制造模式下制造云服务的特征并基于制造领域知识,研究定义了制造云服务的八维QoS评估标准及计算表达式,推导出制造组合云服务的QoS表达,进而建立了制造云服务组合的多目标规划问题模型.最终设计了自适应粒子群算法来解决该多目标规划问题.仿真实验表明,该算法能有效并高效地解决该问题,且求解效率优于传统粒子群算法.
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