Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.eswa.2006.09.018 | Expert Syst. Appl. |
Keywords | Field | DocType |
fuzzy cbr technique,baseline product,fuzzy cbr mechanism,higher ratio,fuzzy cbr,new product development,fuzzy cbr retrieving mechanism,new product idea,product database,fuzzy ahp technique,fuzzy ahp,valuable product idea,case-based reasoning,product idea,case based reasoning,case base reasoning | Data mining,Computer science,Fuzzy set operations,Fuzzy logic,Artificial intelligence,Case-based reasoning,Machine learning,Fuzzy ahp,New product development | Journal |
Volume | Issue | ISSN |
34 | 1 | Expert Systems With Applications |
Citations | PageRank | References |
26 | 1.04 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muh-Cherng Wu | 1 | 227 | 16.58 |
| Ying-Fu Lo | 2 | 32 | 1.54 |
| Shang-hwa Hsu | 3 | 82 | 6.51 |