摘要
工程特性竞争优先度的确定是产品规划质量屋构建的一个关键环节。为了充分反映专家竞争性评估信息的模糊性,提出基于互补梯形模糊偏好的工程特性竞争优先度确定方法。首先,针对产品工程特性竞争性评价信息及专家对于竞争性产品偏好信息的模糊性,提出基于梯形模糊数的工程特性模糊竞争性评价矩阵确定方法及基于梯形模糊数的互补判断矩阵估计方法。其次,逐步规范化相应于每一位专家的工程特性模糊竞争性评价矩阵及互补梯形模糊偏好矩阵,通过一致化二者的模糊评价信息,建立基于总偏差最小化原理的模糊多目标模型以确定相应于每一位专家的工程特性竞争优先度。最后,通过加性加权法集结综合的工程特性竞争优先度,并确定以改进各项工程特性的资源投放为目的的优先性排序。以某机械企业所生产的某型号工业洗衣机的产品改进为例,阐明了本文方法的可行性与有效性。
Determining competitive priorities of engineering characteristics (ECs) is a crucial step to constructing product planning house of quality (PPHOQ). For a purpose of reflecting the fuzziness of competitive evaluation information provided by experts, a method for determining competitive priorities of ECs based on fuzzy complementary trapezoidal preference was proposed. Firstly, since both the competitive evaluation information of ECs and various preferences for each competitive product provided by experts possess fuzziness, an approach for determining competitive evaluations matrices of ECs and an approach for estimating complementary preference matrices based on trapezoidal fuzzy number were presented. Secondly, the competitive evaluation matrix of ECs and complementary trapezoidal preference matrix corresponding to each one of selected experts were both normalized ; the normalized fuzzy information was constructed as a fuzzy multi-objective programming model aiming at determining competitive priorities of ECs corresponding to each expert by using a principle of minimizing a total deviation. Finally, the additive weighted method was employed to acquire competitive priorities of ECs, the improving directions of resources reallocation were then ascertained. The feasibility and effectiveness of the proposed approach was illustrated by a case of industrial washing machine.
出处
《机械设计与研究》
CSCD
北大核心
2015年第4期57-65,共9页
Machine Design And Research
基金
国家自然科学基金资助项目(71371156
70971017)
中央高校基本科研业务费专项资金资助项目(2682014CX001EM)
四川省社科规划项目(SC14B096)
西南交通大学人才引进项目(RC2014-07)
西南交通大学优秀博士学位论文培育项目资助
关键词
工程特性
质量屋
梯形模糊数
互补模糊偏好
Engineering characteristics
House of quality
Trapezoidal fuzzy number
Fuzzy complementary preference