制造资源混合粒度优化组合方案求解技术  

Scheme Solving Technology for Clustering Optimization of Manufacturing Resources with Hybrid Granularities

作　　者：郏维强[1] 冯毅雄[1] 谭建荣[1] 魏喆[1] 安相华[1] 

机构地区：[1]浙江大学流体动力与机电系统国家重点实验室,杭州310027

出　　处：《计算机辅助设计与图形学学报》2012年第3期281-289,共9页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(50835008;51005202);国家"八六三"高技术研究发展计划(2011AA040601)

摘　　要：为实现对云请求端制造需求的快速响应并提供最理想的制造云,在定义制造云服务(manufacturing cloudservice,MCS)的基础上,提出了MCS混合粒度动态优化组合方法.通过对制造服务需求的解析,将MCS按照粒度从大到小的顺序进行匹配,实现了云池资源的初次过滤;然后构建基于总服务成本、服务质量和交易期的制造云多目标优化数学模型,并引入Epsilon-SPEA2优化算法对该优化问题进行求解,从而快速高效地获得Pareto最优解;采用基于改进优劣解距离(technique for order preference by similarity to an ideal solution,TOPSIS)的动态多属性决策方法对Pareto最优解所代表的互为非支配的MCS组合方案进行评价排序,筛选出最优的MCS组合方案.最后结合某区域模具公司群所能提供的制造服务以及相应历史数据,依据客户需求进行MCS的动态优化组合仿真,验证了文中方法的可行性和实用性.In order to realize rapid response to manufacturing needs and provide with the most ideal manufacturing cloud services （MCS）, a dynamic clustering optimization approach for multi-granularity MCS is proposed. An analytical framework is given to organize manufacturing cloud services in the order of granularity scales through the decomposition of manufacturing needs, thus the cloud resource pool is filtered for the first time. A multi-objective model based on total cost, service quality and delivery time is constructed and solved by Epsilon-SPEA2 so that Pareto optimal solutions can be obtained. An improved TOPSIS technique is employed to assess the solutions which represent the clustering schemes, thus the best clustering scheme can be selected. A practical example related to a project in mould industry is given to validate its effectiveness and feasibility.

关 键 词：制造云服务 混合粒度 制造云 优化组合 Epsilon-SPEA2 优劣解距离法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]