面向产品族的客户需求最佳聚类确定方法

以大规模定制为前提,为实现客户需求指导产品族规划的目的,建立了客户需求广义聚类模型．根据客户需求信息是否存在层次等级关系将其划分为递阶型和等价型,针对目前对递阶型客户需求聚类研究不能充分利用需求信息的问题,提出了基于模糊集的混合型客户需求聚类算法,增强了处理需求信息的能力．为实现产品族的合理规划,综合客户满意度和企业成本理想度建立可行性指标,提出了基于可行性确定客户需求最佳聚类的方法,并以最佳聚类方案指导产品族规划,在满足客户需求的同时保证了企业宽松的资金链．最后结合实例说明了该方法的实用性．     

面向产品族的模块化设计方法研究

为使模块化设计中模块之间的功能独立,在基于功能相关与结构相关的模块划分准则的基础上进行模糊聚类,通过设定阀值确定模块划分结果,并在此基础上对划分的模块进行变型设计,组成产品族,为客户的个性化需求提供产品设计方法．     

面向产品族协同优化设计的模糊双层规划模型

针对含有不确定参数的产品族协同优化设计问题中的一种基本类型——产品参数与产品功能之间的映射含有模糊系数并且平台参数和个性化参数间具有一定的主从关系的情形，建立了一种对平台参数和非平台参数进行协同优化的模糊双层线性规划模型，并对其决策机制和求解方法进行了研究，给出一个数值算例。     

基于模块化产品族与客户需求转化的产品配置

建立了模块化产品族结构,将产品族分解为类模块,为每一类模块建立一个模块模型,以属性变量表示各模块模型.利用QFD思想建立从客户需求到模块属性的映射,确定客户所需产品的各模块属性变量取值和权重.然后在同类模块的范围内检索出与客户需求最相近的模块实例集,再纳入各模块及属性之间的相关约束,有效地组合模块,避免大量无效配置,提高产品配置效率.     

基于自然语言理解的需求聚类和需求优选方法研究

为了实现大数量软件需求的优选,提出了一种基于自然语言理解的需求聚类和需求优选方法。该方法首先基于自然语言处理技术对需求进行深层次语言理解,找到相同语义的需求并只保留一条。随后,采用基于超图分割的需求聚类方法对大量需求进行聚类。最后,提出了需求优选目标函数,该目标函数根据需求聚类结果为需求优先级打分,并在综合考虑需求与类别的相似性、类别的权重以及聚类标准的权重的基础上,最终给出一个合理的需求优选结果。在大规模真实的需求集合上进行了实验,实验结果表明:基于自然语言处理技术的需求聚类性能优异;需求聚类对后续的需求优选起有着非常重要的作用;基于超图分割的需求聚类,以及在此基础上提出的需求优选函数相对于基线方法有着明显的提高。     

通用电机产品族协同设计的模糊优化

产品族设计是大规模定制中的核心内容,许多文献在假定产品特征确定情况下,采用平台参数和非平台参数,提出了大规模定制下的产品族设计优化方法。基于产品族设计理念,针对含有不确定特征的产品族设计中的一种基本类型——产品参数与产品功能之间的映射含有模糊系数的情形,建立一种对平台参数和非平台参数进行协同优化的模糊双层线性规划模型,利用有关模糊算法对模糊条件下的通用电机产品族协同设计进行优化。     

基于需求层次模型的产品发展趋势预测方法

提出了一种从用户产品需求进化规律来定位目前产品发展阶段,寻找产品改进方向,预测未来发展趋势的方法。该方法建立了用户需求层次模型和用户需求到产品特性之间的映射关系,采用模糊聚类分析法对产品特征进行聚类分析,并用分析结果进行产品预测。最后以插线板特征分析为例,验证了该方法的可行性。     

基于模糊聚类的RBF神经网络算法

文章提出一种根据模糊聚类的思想来确定RBF神经网络隐层节点数,并用K-Means的聚类算法来训练RBF神经网络.并根据此算法进行仿真,并证明是有效的.     

基于多目标和聚类的产品功能疲劳健壮性分析

研究了产品功能疲劳问题中产品功能组合的健壮性分析问题,提出了基于NSGA-Ⅱ和客户聚类的综合分析方法.具体包括建立了基于NSGA-Ⅱ算法的产品功能疲劳问题的多目标优化模型,并利用K-Means算法对客户评价数据进行了聚类分析,然后结合多目标模型和聚类结果进行健壮性分析,为产品开发人员选择健壮性的产品功能组合提供决策支持.最后通过智能手机产品的实际案例说明了所提出方法的有效性.     

基于Kano模型的原型模式系统需求排序方法研究

针对当前国内很多小规模的互联网公司仍然采用加性加权法进行需求排序难以保证用户的满意度的问题,提出了一种基于Kano模型的原型模式系统需求排序方法。有别于传统的加性加权法,该方法将F相似关系聚类方法和粗数分析引入到Kano模型定量分析中,以便对需求研发优先级进行排序。它可以实现需求工程师识别并确定用户对原型相应实现程度的满意程度, 并在此基础上保证需求完整, 以达到需求排序符合用户期望的目的。     

基于结合分析的产品意象造型设计研究

为设计出符合消费者感性需求的产品,提出基于结合分析的产品意象造型设计方法。首先采用多元尺度法和聚类分析法选择典型样本,进而应用形态分析法确定产品的属性和各属性的水平。其次,利用因子分析法确定产品的感性意象,并用造型吸引力对其进行整合。在此基础上,使用对应分析法对感性意象和产品属性的关系进行探讨。最后,利用结合分析建立二者之间的数学模型。该文结合办公座椅进行研究,结果表明该方法是正确可行的。     

基于模糊集表达和熵处理的产品用户需求评价

 在产品设计早期,对用户需求功能及其重要程度进行分析和获取的成功与否直接关系到产品设计目标特性的质量以及后续产品开发的各个阶段。提出一种基于模糊集表达和熵处理的有效用户需求获取方法。根据用户需求调查结果,建立了用户需求功能抽象的原则,采用模糊矩阵对各用户需求进行表达,并用熵处理的方法简化用户需求模糊矩阵,从而获取用户需求的重要度排序。该方法的显著优点是能够反应用户需求的主观性和模糊性。     

基于基因工程思想的产品族设计及其关键技术研究

通过分析型材断面的尺寸和形状特征的继承关系及型材模块的结构变异,将基因工程的思想用于产品族的开发设计中,并将"型材特征"看作是产品结构信息的"DNA"。借鉴生物学领域基因工程原理,揭示了"型材特征"这种信息流在产品树中的传播规律,在此基础上提出了一种基于基因工程思想的产品基因编码与重组方法。通过对产品族和产品基因DNA的概念以及产品族设计DNA的内涵和研究内容的探讨,指出了支持产品基因工程的关键技术,并以门体型材为例,说明产品族基因工程思想的设计效果。     

Integrating Kansei engineering with conjoint analysis to fulfil market segmentation and product customisation for digital cameras

Diverse customer desires coupled with technological advances have forced companies to manufacture products with ultimate performance, low cost, high quality and much shorter time-to-market. Recently, the popularity of smart phones has given rise to seriously declined product sales of digital cameras. In this paper, a two-phase framework is presented to offer decision supports on developing next-generation cameras. In the phase of market segmentation, Kansei engineering is employed to capture customer perceptions of affective features. Then, rough set theory is conducted to generate decision rules for partitioning the whole market into the consumer segment and the professional segment, respectively. In the phase of product customisation, conjoint analysis is applied to extract customer preferences for functional features. Furthermore, Grey relational analysis is conducted to select the top three varieties with regard to two distinct segments. In particular, this paper is capable to help brand companies or camera manufacturers better capture customer perceptions and preferences for digital cameras, effectively perform market segmentation (based on affective features) and efficiently conduct product customisation (based on functional features).     

High-end equipment customer requirement analysis based on opinion extraction

Acquisition and analysis of customer requirements are the essential steps in high-end equipment design. Considering that Internet and big data technologies are integrated into the manufacturing industry, we propose a method of analyzing customer requirements based on open-source data. First, online data are collected with focused crawlers and preprocessed to filter noise and duplicate. Then, user opinions are extracted based on the defined template, and users’ sentiments are analyzed. Based on the relationship between user sentiments and attribute parameters, the parameter range that satisfies customers can be obtained. The proposed method is evaluated by using an example of new energy vehicle to verify its availability and feasibility.     

Joint decision of product configuration and remanufacturing for product family design

Product family design (PFD) is a popular method for increasing product variety to satisfy the needs of diversified markets. With the increasing concern for environmental friendliness in society, more and more companies develop launching remanufactured products and include them in their product families. Therefore, PFD should be considered in a broad decision space where configuration of product variants and remanufacturing are considered simultaneously. However, this issue was not addressed properly in previous research. In this paper, a methodology for joint decision of product configuration and remanufacturing is proposed in which a bi-objective mixed integer programming model is formulated to determine the configurations of both new and remanufactured products for minimising product cost, maximising total market share, and satisfying reliability requirements. Then, Non-dominated Sorting Genetic Algorithm II (NSGAII) is adopted to solve the optimization problem. Computational experiments were conducted and their results show that NSGAII is convergent to the model well. A case study is presented to illustrate the applicability and effectiveness of the proposed methodology.     

Modularity analysis and commonality design: a framework for the top-down platform and product family design

With a highly fragmented market and increased competition, platform-based product family design is recognised as an effective method for constructing a product line that satisfies diverse customer demand while keeping design and production cost- and time-effective. Recognising the need for modularity and commonality in platform development, this paper presents a systematic framework to assist in implementing top-down platform and product family design, which aims to achieve system-level modularity for variety generation, and rationalise the commonality configuration for module instantiation. In the first phase of platform development, a robust and flexible product family architecture is constructed to accommodate variations by analysing the external varieties of the generic product architecture, and provide a modularity design space, wherein the design tasks are further decomposed into module instantiation. The second phase of detailed platform development aims to enhance commonality in terms of engineering efficiency by coordinating with the back-end product realisation stage. A tractable optimisation method is used to capture and resolve the trade-off between commonality configuration and individual product performance. A family of power tool designs is used to demonstrate the potential and feasibility of the proposed framework at the system level and detailed design stages.     

基于功能矩阵空间和变换法的产品设计需求功能获取

 针对产品概念设计阶段获取的用户需求功能,构建了设计需求功能获取原理框架和用户知识空间定性模型,并在功能范畴下以基本功能集为组成单元,以包含运动、能量、控制、关联、综合这5种类型基本功能集的用户需求功能矩阵表达为表现形式,建立了空间变换和运算法则及其判定准则,并在变换运算过程中采用三维模型二维化表达方法,将三维空间和变换运算转换为二维平面和变换运算,使得从用户需求功能到设计需求功能的变换可以伴随用户知识空间到设计者知识空间的变换实现,从而得到了包含各项基本功能集子空间的设计需求功能。通过一个实例贯穿在各个部分中来验证理论和方法的可行性,最后给出了相应的结论和未来需要进一步研究的内容。     

A combined QFD and integer programming framework to determine attribute levels for conjoint study

Conjoint Analysis (CA) and Quality Function Deployment (QFD) are two popular tools for new product design; marketers frequently use the former and engineers the latter. Typically, in a conjoint study, the attributes and their levels are determined through focus group discussions or market surveys. Sometimes, the market researchers exclude some critical features or include unrealistic attribute levels resulting in infeasible product profiles. Inappropriate selection of attribute levels may render the conjoint study less useful. In QFD, the New Product Development team attempts to identify the technical characteristics (TCs) to be improved (included) to meet the customer requirements (CRs) through a subjective relationship matrix between CRs and TCs. At present there is no methodology that uses the output of QFD to generate feasible product profiles to be used in CA and therefore improve its usefulness. In this paper, QFD is used along with an integer programming (IP) model to determine the appropriate TCs and consequently the right attribute levels. These attribute levels are then used in a conjoint study. It is also proposed to measure the elements of the so-called relationship matrix in QFD in a way so that the right levels of the attributes can be generated from the IP solution. The proposed method is illustrated through a commercial vehicle design problem with hypothetical data.