Evaluation and selection in product design for mass customization: A knowledge decision support approach
Evaluation and selection in product design for mass customization: A knowledge decision support approach
复制标题
DOI：
10.1017/s0890060404040077
复制
发表时间：
2004-02
期刊：
影响因子：
--
通讯作者：
X. Zha;Ram D. Sriram;W. Lu
中科院分区：
文献类型：
--
作者：
X. Zha;Ram D. Sriram;W. Lu
大规模定制已被越来越多的企业确定为一种竞争战略。基于族的产品设计是支持大规模定制生产的一种高效、有效的手段，可以在满足客户需求的同时实现产品的充分多样性。提出了一种面向大规模定制的产品族设计评价与选择的知识决策支持方法。在这里，产品族设计被看作是一个选择问题的以下阶段：产品族（设计方案）生成，产品族设计评价，选择定制。讨论了面向大规模定制的产品族设计的基本问题。在此基础上，提出了面向大规模定制的基于模块的产品族设计知识支持框架及其相关技术。提出了一种系统的模糊聚类和排序模型，并进行了详细的讨论。该模型支持具有模糊客户偏好关系的决策固有的不精确性，并使用模糊分析技术进行评估和选择。采用神经网络技术对隶属函数进行调整，以提高模型的精度。本文的重点是开发一个知识密集型的支持计划和一个全面系统的模糊聚类和排序方法的产品族设计评价和选择。最后，以一个电源产品族评估、选择与定制知识支持的案例和场景为例进行了说明。
Mass customization has been identified as a competitive strategy by an increasing number of companies. Family-based product design is an efficient and effective means to realize sufficient product variety, while satisfying a range of customer demands in support for mass customization. This paper presents a knowledge decision support approach to product family design evaluation and selection for mass customization process. Here, product family design is viewed as a selection problem with the following stages: product family (design alternatives) generation, product family design evaluation, and selection for customization. The fundamental issues underlying product family design for mass customization are discussed. Then, a knowledge support framework and its relevant technologies are developed for module-based product family design for mass customization. A systematic fuzzy clustering and ranking model is proposed and discussed in detail. This model supports the imprecision inherent in decision making with fuzzy customers' preference relations and uses fuzzy analysis techniques for evaluation and selection. A neural network technique is also adopted to adjust the membership function to enhance the model. The focus of this paper is on the development of a knowledge-intensive support scheme and a comprehensive systematic fuzzy clustering and ranking methodology for product family design evaluation and selection. A case study and the scenario of knowledge support for power supply family evaluation, selection, and customization are provided for illustration.