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Mathematical Problems in Engineering
Volume 2017, Article ID 5187521, 15 pages
https://doi.org/10.1155/2017/5187521
Research Article

Product Form Design Model Based on Multiobjective Optimization and Multicriteria Decision-Making

1Department of Industrial Design, National Cheng Kung University, No. 1 University Road, Tainan 70101, Taiwan
2Department of Multimedia and Entertainment Science, Southern Taiwan University of Science and Technology, No. 1 Nantai Street, Yungkang District, Tainan 71005, Taiwan

Correspondence should be addressed to Yongfeng Li; moc.liamtoh@rdilfy

Received 18 July 2016; Accepted 28 November 2016; Published 11 January 2017

Academic Editor: Thomas Hanne

Copyright © 2017 Meng-Dar Shieh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Affective responses concern customers’ affective needs and have received increasing attention in consumer-focused research. To design a product that appeals to consumers, designers should consider multiple affective responses (MARs). Designing products capable of satisfying MARs falls into the category of multiobjective optimization (MOO). However, when exploring optimal product form design, most relevant studies have transformed multiple objectives into a single objective, which limits their usefulness to designers and consumers. To optimize product form design for MARs, this paper proposes an integrated model based on MOO and multicriteria decision-making (MCDM). First, design analysis is applied to identify design variables and MARs; quantification theory type I is then employed to build the relationship models between them; on the basis of these models, an MOO model for optimization of product form design is constructed. Next, we use nondominated sorting genetic algorithm-II (NSGA-II) as a multiobjective evolutionary algorithm (MOEA) to solve the MOO model and thereby derive Pareto optimal solutions. Finally, we adopt the fuzzy analytic hierarchy process (FAHP) to obtain the optimal design from the Pareto solutions. A case study of car form design is conducted to demonstrate the proposed approach. The results suggest that this approach is feasible and effective in obtaining optimal designs and can provide great insight for product form design.