A fuzzy CBR technique for generating product ideas
A fuzzy CBR technique for generating product ideas
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DOI：
10.1016/j.eswa.2006.09.018
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Muh-Cherng Wu;Ying-Fu Lo;S. Hsu
中科院分区：
文献类型：
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作者：
Muh-Cherng Wu;Ying-Fu Lo;S. Hsu
本文提出了一种模糊CBR（基于案例的推理）技术，用于从产品数据库中生成新的产品创意，以增强给定产品（称为基线产品）的功能。在数据库中，一个产品由100个属性向量建模，其中87个用于对使用场景建模，13个用于描述制造/回收特性。基于模糊层次分析法确定的使用场景属性及其相对权重，开发了一种模糊CBR检索机制，以检索倾向于增强基线产品功能的产品思想。基于制造/回收的特点，建立了模糊CBR机制对检索到的产品创意进行筛选，以获得更高比例的有价值的产品创意。实验表明，检索和过滤机制在产生更高比例的有价值的产品创意方面优于先前的仅检索机制。
This paper presents a fuzzy CBR (case-based reasoning) technique for generating new product ideas from a product database for enhancing the functions of a given product (called the baseline product). In the database, a product is modeled by a 100-attribute vector, 87 of which are used to model the use-scenario and 13 are used to describe the manufacturing/recycling features. Based on the use-scenario attributes and their relative weights – determined by a fuzzy AHP technique, a fuzzy CBR retrieving mechanism is developed to retrieve product-ideas that tend to enhance the functions of the baseline product. Based on the manufacturing/recycling features, a fuzzy CBR mechanism is developed to screen the retrieved product ideas in order to obtain a higher ratio of valuable product ideas. Experiments indicate that the retrieving-and-filtering mechanism outperforms the prior retrieving-only mechanism in terms of generating a higher ratio of valuable product ideas.