Heuristics for product-line design using conjoint analysis
Management Science
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Expert Systems
Computers in Industry - Stimulating manufacturing excellence in small and medium enterprises
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Special Section: Topological representation and reasoning in design and manufacturing
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
An investigation into affective design using sorting technique and Kohonen self-organising map
Advances in Engineering Software
PDCS-a product definition and customisation system for product concept development
Expert Systems with Applications: An International Journal
International Journal of Computer Integrated Manufacturing
Evaluation and management of new service concepts: An ANP-based portfolio approach
Computers and Industrial Engineering
A functional-commercial analysis strategy for product conceptualization
Expert Systems with Applications: An International Journal
A quality-time-cost-oriented strategy for product conceptualization
Advanced Engineering Informatics
Expert Systems with Applications: An International Journal
Computer Standards & Interfaces
Affective and cognitive design for mass personalization: status and prospect
Journal of Intelligent Manufacturing
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Product conceptualization is regarded as a key activity in new product development (NPD). In this stage, product concept generation and selection plays a crucial role. This paper presents a product concept generation and selection (PCGS) approach, which was proposed to assist product designers in generating and selecting design alternatives during the product conceptualization stage. In the PCGS, general sorting was adapted for initial requirements acquisition and platform definition; while a fuzzy c-means (FCM) algorithm was integrated with a design alternatives generation strategy for clustering design options and selecting preferred product concepts. The PCGS deliberates and embeds a psychology-originated method, i.e., sorting technique, to widen domain coverage and improve the effectiveness in initial platform formation. Furthermore, it successfully improves the FCM algorithm in such a way that more accurate clustering results can be obtained. A case study on a wood golf club design was used for illustrating the proposed approach. The results were promising and revealed the potential of the PCGS method.