针对未来智能化无人化作战特点,结合OODA环理论给出平行作战概念,建立了平行作战系统模型。同时,针对传统质量功能展开(quality function Deployment,QFD)方法和耦合协调度分析在需求分析过程中存在的可靠性低、客观性不足、描述角度单...针对未来智能化无人化作战特点,结合OODA环理论给出平行作战概念,建立了平行作战系统模型。同时,针对传统质量功能展开(quality function Deployment,QFD)方法和耦合协调度分析在需求分析过程中存在的可靠性低、客观性不足、描述角度单一等问题,建立了基于灰关联分析的QFD(grey quality function deployment,GQFD)耦合协调度需求分析模型,以及基于灰色关联分析的平行作战系统能力需求分析质量屋,得到了平行作战系统能力需求重要度排序。对平行作战系统能力的耦合协调情况进行了分析,为平行作战系统的需求分析提供了更为客观的方法,为平行作战系统的构建提供了理论依据。展开更多
针对当前装备保障能力需求分析方法无法满足智能化发展需要,存在主观性强、可靠性不高等问题,采用基于灰关联分析的质量功能展开方法(grey quality function deployment,GQFD),在对智能化装备保障“任务-能力”分析的基础上,建立了基于...针对当前装备保障能力需求分析方法无法满足智能化发展需要,存在主观性强、可靠性不高等问题,采用基于灰关联分析的质量功能展开方法(grey quality function deployment,GQFD),在对智能化装备保障“任务-能力”分析的基础上,建立了基于灰关联分析的需求质量屋模型,得到了智能化装备保障能力需求指标重要度排序,为智能化装备保障力量建设与运用提供了理论依据。展开更多
Quality function deployment （QFD） is a well-known customer-oriented product design methodology. Rating the final importance of customer requirements （CRs） is really a very es- sential starting point in the impleme...Quality function deployment （QFD） is a well-known customer-oriented product design methodology. Rating the final importance of customer requirements （CRs） is really a very es- sential starting point in the implementation of QFD, since it largely affects the target setting value of design requirements. This pa- per aims to propose a novel method to deal with the relative importance ratings （RIRs） of CRs problem considering customers＇ diversified requirements and unknown information on customers＇ weights, which is an indispensable process for determining the final importance ratings of CRs. First, a new concept of customer＇s assessment structure is proposed according to the basic idea of grey relational analysis （GRA）, and then a constrained nonlinear optimization model is constructed to describe the assessment information aggregation factors of CRs considering customers＇ personalized and diversified requirements. Furthermore, an im- mune particle swarm optimization （IPSO） algorithm is designed to solve the model, and the weight vector of customers is obtained. Finally, a car door design example is introduced to illustrate the novel hybrid GRA-IPSO method＇s potential application in deter- mining the RIRs of CRs.展开更多
文摘针对未来智能化无人化作战特点,结合OODA环理论给出平行作战概念,建立了平行作战系统模型。同时,针对传统质量功能展开(quality function Deployment,QFD)方法和耦合协调度分析在需求分析过程中存在的可靠性低、客观性不足、描述角度单一等问题,建立了基于灰关联分析的QFD(grey quality function deployment,GQFD)耦合协调度需求分析模型,以及基于灰色关联分析的平行作战系统能力需求分析质量屋,得到了平行作战系统能力需求重要度排序。对平行作战系统能力的耦合协调情况进行了分析,为平行作战系统的需求分析提供了更为客观的方法,为平行作战系统的构建提供了理论依据。
文摘针对当前装备保障能力需求分析方法无法满足智能化发展需要,存在主观性强、可靠性不高等问题,采用基于灰关联分析的质量功能展开方法(grey quality function deployment,GQFD),在对智能化装备保障“任务-能力”分析的基础上,建立了基于灰关联分析的需求质量屋模型,得到了智能化装备保障能力需求指标重要度排序,为智能化装备保障力量建设与运用提供了理论依据。
基金supported by the Fundamental Research Funds for the Central Universities(K5051399035BDY251412+1 种基金JB150601)the Soft Science Project of Shaanxi Province(2013KRZ25)
文摘Quality function deployment （QFD） is a well-known customer-oriented product design methodology. Rating the final importance of customer requirements （CRs） is really a very es- sential starting point in the implementation of QFD, since it largely affects the target setting value of design requirements. This pa- per aims to propose a novel method to deal with the relative importance ratings （RIRs） of CRs problem considering customers＇ diversified requirements and unknown information on customers＇ weights, which is an indispensable process for determining the final importance ratings of CRs. First, a new concept of customer＇s assessment structure is proposed according to the basic idea of grey relational analysis （GRA）, and then a constrained nonlinear optimization model is constructed to describe the assessment information aggregation factors of CRs considering customers＇ personalized and diversified requirements. Furthermore, an im- mune particle swarm optimization （IPSO） algorithm is designed to solve the model, and the weight vector of customers is obtained. Finally, a car door design example is introduced to illustrate the novel hybrid GRA-IPSO method＇s potential application in deter- mining the RIRs of CRs.
