产品设计的模糊质量功能配置技术研究
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摘要
质量功能配置是面向用户的系统化产品设计方法，通过质量屋矩阵转换方式，将用户需求逐层分解为各阶段设计和制造要求，实现产品的并行开发。为了解决产品早期设计阶段质量功能配置中存在的输入信息不确定性和判断的主观性所带来的本质模糊性问题，本文以质量屋信息模糊化处理为主线，对模糊环境下的质量屋分析与决策技术进行了深入系统的研究。
本文在分析传统质量功能配置方法采用精确数字标度无法有效处理模糊性问题的不足基础上，基于质量屋输入信息的语言变量表达形式和质量屋构建过程的主观评估方式，建立了完整的模糊质量屋理论框架，并围绕质量屋建立与分析中各部分的信息模糊化处理以及模糊环境下的质量屋决策研究解决方法。
用户需求的提取与分析是质量功能配置的关键环节，规范化流程和结构化表达是信息获取的有效方法，利用模糊动态聚类方法可以实现用户需求项的层次化管理。重要度是质量屋的基本输入信息，本文分析了主观确定和客观确定方法的片面性，提出了一个综合主客观因素的模糊评判逆过程方法，利用客观信息建立模糊关系方程，以主观信息推导评判结果，按择近原则确定近似的权重分配。该方法能够有效利用容易获取的主客观信息，实例研究结果证明了可行性。
技术特征重要度排序是质量屋的重要输出信息，本文针对采用模糊序关系进行两两对比方法计算复杂问题，提出了一种改进方法，通过建立一致的参照基准，定义效用函数，大幅减少了计算次数，解决了多个技术特征模糊重要度排序效率问题，提高了排序的可靠性。
技术特征目标值的配置是一个复杂的多变量、多目标决策过程。本文分析了质量屋技术特征配置存在的模糊因素，提出了一个综合考虑模糊目标、模糊约束和模糊系数的可能性线性规划模型，克服了局部模糊优化的不完整性。仿真结果表明，该方法获得的技术特征优化配置结果是一个随着可信性和可能性不同而变化的改进区间。
产品设计是一个多方案评估与择优过程。为了使质量屋能够用于方案评估，用方案排序矩阵替换技术竞争性矩阵，对质量屋结构进行了改进，以关系矩阵联系用户需求与方案技术特征，实现面向用户的技术方案评估。为了解决信息不完备条件下的方案评估问题，提出了一个基于质量屋的模糊方案优选模型。首次提出直接利用质量屋输出的技术特征模糊权重和设计方案定性、定量指标建立模糊决策矩阵，以海明距离为测度工具，采用模糊折衷方法进行模糊多属性决策，实现了早期设计阶段的方案优先级排序。仿真研究验证了方法的可行性和有效性。
质量功能配置的多功能小组工作模式体现了群决策特点。本文研究了知识协同的原理和方法，确定了质量屋群决策模型的先综合评估结果再统一决策原则。充分考虑质量功能配置过程内在模糊性和成员偏好差异，提出了一个基于模糊群决策的质量屋方案评估模型，通过群体信息综合加权和指标加权，实现信息协同，计算各方案与模糊理想解的相对贴近度确定方案的优先次序。仿真结果表明，该模型为质量屋应用于设计方案择优提供了实用化方法。
Quality function deployment (QFD) is a systematic customer-oriented method of product design. Through matrices named 'House of Quality' (HOQ) translation, Customer requirements are broken down into each stage of designing and manufacturing requirements step by step, and then the concurrent engineering of product development is put into practice. To solve the intrinsic fuzziness problem that is brought about by indefinite input information and subjective evaluation in the early stage of product design, through fuzzing information in HOQ which is the main clue of this article, the analytic and decision-making technology of HOQ in fuzzy environment is studied deeply and systematically.
Based on analyzing the deficiency that traditional QFD which adopted exact digital scale cannot tackle the fuzzy problem effectively, according to linguistic variable expression style of HOQ input information and subjective evaluation way of HOQ constructing process, the comprehensive theoretic framework of fuzzy HOQ is founded. Surrounding each part of data fuzzing in building and analyzing process of HOQ and decision-making research of HOQ in the fuzzy environment, resolution methods are studied.
Drawing and analyzing the customer requirements is the critical step in QFD. Normalized process and constructed expression method are the effective way. Utilizing fuzzy analytic cluster method can realize customer requirement items levels management. Customer requirement weights are the basic input information of HOQ. In this article, one-sided character is analyzed in subjective determination or objective determination method separately, and a reversal process method of fuzzy evaluation integrating subjective and objective factors is presented. The objective information is used to found the fuzzy relation equation. The subjective information is applied to deduce evaluation result. According to the principle of closeness optimization, the approximate weights are determined. The method can effectively use the subjective and objective information what can be captured easily. The example result proved the feasibility.
Ranking the technical characteristic weights is the important output information of HOQ. Aiming to the problem that adopted fuzzy order relationship method to compare couples conducting complexly, this article brings forward an improved method. By founding uniform benchmark to refer and defining utility function, computing burden decreases largely. The problem of deficiency on ranking multiple technical characteristic fuzzy weights is solved. The reliability is increased.
The deployment of technical characteristic target value is a complicated process with multiple variables and multiple objectives. Existent fuzzy factors in technical characteristic deployment of HOQ are analyzed in this article. A possibility linear program model, which comprehensively considers fuzzy objectives, fuzzy restricts and fuzzy coefficients, is presented. Incompleteness of partial fuzzy optimization is conquered. The simulation result shows that the acquired technical characteristic optimum deployment result is a changed improved region that comes along with the credibility and possibility.
Product design is a process of multi-scheme evaluation and optimization selection. In order to supply HOQ to schemes evaluation, the structure of HOQ is reformed through substituting the schemes ranking matrix for the technical competitive analysis matrix. The relationship matrix is used to connect customer requirements to technical characteristics of schemes, and then customer-oriented schemes evaluation is realized. For solving the problem of schemes evaluation in the condition of incomplete information, a fuzzy schemes selection model based on HOQ is given. For the first time, the fuzzy technical characteristic weights outputted from HOQ and the quantitative or qualitative technical values of design schemes are utilized directly to found a fuzzy decision-making matrix. Hamming distance is used as measure tool. Fuzzy compromise method is adopted to implement fuzzy multi-attribute decision-making. Then, the aim of prioritizing the schemes in the early design stage is realized. Simulation study proved the feasibility and usefulness.
Multi-functional team working pattern of QFD embodies group decision-making characteristics. In this article, the author researches the principles and methods of knowledge collaboration, and determines the rule of integrating evaluation results in advance and united decision-making afterwards. Considering inherent fuzziness of QFD process and each individual member's different preferences sufficiently, a scheme evaluation model using HOQ based on fuzzy group decision-making is put forward. Through weighting the integrated group information and weighting technical values, knowledge collaboration is carried out. The ranking of the schemes is determined by computing each scheme's relative closeness degree to fuzzy ideal solution. Simulation research shows that the model gives the practical way to apply the HOQ to designing schemes selection.
本文在分析传统质量功能配置方法采用精确数字标度无法有效处理模糊性问题的不足基础上，基于质量屋输入信息的语言变量表达形式和质量屋构建过程的主观评估方式，建立了完整的模糊质量屋理论框架，并围绕质量屋建立与分析中各部分的信息模糊化处理以及模糊环境下的质量屋决策研究解决方法。
用户需求的提取与分析是质量功能配置的关键环节，规范化流程和结构化表达是信息获取的有效方法，利用模糊动态聚类方法可以实现用户需求项的层次化管理。重要度是质量屋的基本输入信息，本文分析了主观确定和客观确定方法的片面性，提出了一个综合主客观因素的模糊评判逆过程方法，利用客观信息建立模糊关系方程，以主观信息推导评判结果，按择近原则确定近似的权重分配。该方法能够有效利用容易获取的主客观信息，实例研究结果证明了可行性。
技术特征重要度排序是质量屋的重要输出信息，本文针对采用模糊序关系进行两两对比方法计算复杂问题，提出了一种改进方法，通过建立一致的参照基准，定义效用函数，大幅减少了计算次数，解决了多个技术特征模糊重要度排序效率问题，提高了排序的可靠性。
技术特征目标值的配置是一个复杂的多变量、多目标决策过程。本文分析了质量屋技术特征配置存在的模糊因素，提出了一个综合考虑模糊目标、模糊约束和模糊系数的可能性线性规划模型，克服了局部模糊优化的不完整性。仿真结果表明，该方法获得的技术特征优化配置结果是一个随着可信性和可能性不同而变化的改进区间。
产品设计是一个多方案评估与择优过程。为了使质量屋能够用于方案评估，用方案排序矩阵替换技术竞争性矩阵，对质量屋结构进行了改进，以关系矩阵联系用户需求与方案技术特征，实现面向用户的技术方案评估。为了解决信息不完备条件下的方案评估问题，提出了一个基于质量屋的模糊方案优选模型。首次提出直接利用质量屋输出的技术特征模糊权重和设计方案定性、定量指标建立模糊决策矩阵，以海明距离为测度工具，采用模糊折衷方法进行模糊多属性决策，实现了早期设计阶段的方案优先级排序。仿真研究验证了方法的可行性和有效性。
质量功能配置的多功能小组工作模式体现了群决策特点。本文研究了知识协同的原理和方法，确定了质量屋群决策模型的先综合评估结果再统一决策原则。充分考虑质量功能配置过程内在模糊性和成员偏好差异，提出了一个基于模糊群决策的质量屋方案评估模型，通过群体信息综合加权和指标加权，实现信息协同，计算各方案与模糊理想解的相对贴近度确定方案的优先次序。仿真结果表明，该模型为质量屋应用于设计方案择优提供了实用化方法。
Quality function deployment (QFD) is a systematic customer-oriented method of product design. Through matrices named 'House of Quality' (HOQ) translation, Customer requirements are broken down into each stage of designing and manufacturing requirements step by step, and then the concurrent engineering of product development is put into practice. To solve the intrinsic fuzziness problem that is brought about by indefinite input information and subjective evaluation in the early stage of product design, through fuzzing information in HOQ which is the main clue of this article, the analytic and decision-making technology of HOQ in fuzzy environment is studied deeply and systematically.
Based on analyzing the deficiency that traditional QFD which adopted exact digital scale cannot tackle the fuzzy problem effectively, according to linguistic variable expression style of HOQ input information and subjective evaluation way of HOQ constructing process, the comprehensive theoretic framework of fuzzy HOQ is founded. Surrounding each part of data fuzzing in building and analyzing process of HOQ and decision-making research of HOQ in the fuzzy environment, resolution methods are studied.
Drawing and analyzing the customer requirements is the critical step in QFD. Normalized process and constructed expression method are the effective way. Utilizing fuzzy analytic cluster method can realize customer requirement items levels management. Customer requirement weights are the basic input information of HOQ. In this article, one-sided character is analyzed in subjective determination or objective determination method separately, and a reversal process method of fuzzy evaluation integrating subjective and objective factors is presented. The objective information is used to found the fuzzy relation equation. The subjective information is applied to deduce evaluation result. According to the principle of closeness optimization, the approximate weights are determined. The method can effectively use the subjective and objective information what can be captured easily. The example result proved the feasibility.
Ranking the technical characteristic weights is the important output information of HOQ. Aiming to the problem that adopted fuzzy order relationship method to compare couples conducting complexly, this article brings forward an improved method. By founding uniform benchmark to refer and defining utility function, computing burden decreases largely. The problem of deficiency on ranking multiple technical characteristic fuzzy weights is solved. The reliability is increased.
The deployment of technical characteristic target value is a complicated process with multiple variables and multiple objectives. Existent fuzzy factors in technical characteristic deployment of HOQ are analyzed in this article. A possibility linear program model, which comprehensively considers fuzzy objectives, fuzzy restricts and fuzzy coefficients, is presented. Incompleteness of partial fuzzy optimization is conquered. The simulation result shows that the acquired technical characteristic optimum deployment result is a changed improved region that comes along with the credibility and possibility.
Product design is a process of multi-scheme evaluation and optimization selection. In order to supply HOQ to schemes evaluation, the structure of HOQ is reformed through substituting the schemes ranking matrix for the technical competitive analysis matrix. The relationship matrix is used to connect customer requirements to technical characteristics of schemes, and then customer-oriented schemes evaluation is realized. For solving the problem of schemes evaluation in the condition of incomplete information, a fuzzy schemes selection model based on HOQ is given. For the first time, the fuzzy technical characteristic weights outputted from HOQ and the quantitative or qualitative technical values of design schemes are utilized directly to found a fuzzy decision-making matrix. Hamming distance is used as measure tool. Fuzzy compromise method is adopted to implement fuzzy multi-attribute decision-making. Then, the aim of prioritizing the schemes in the early design stage is realized. Simulation study proved the feasibility and usefulness.
Multi-functional team working pattern of QFD embodies group decision-making characteristics. In this article, the author researches the principles and methods of knowledge collaboration, and determines the rule of integrating evaluation results in advance and united decision-making afterwards. Considering inherent fuzziness of QFD process and each individual member's different preferences sufficiently, a scheme evaluation model using HOQ based on fuzzy group decision-making is put forward. Through weighting the integrated group information and weighting technical values, knowledge collaboration is carried out. The ranking of the schemes is determined by computing each scheme's relative closeness degree to fuzzy ideal solution. Simulation research shows that the model gives the practical way to apply the HOQ to designing schemes selection.
引文
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