Novel Method for Perceiving Key Requirements of Customer Collaboration Low-Carbon Product Design

Low-carbon product design is an important way to reduce greenhouse gas emission. Customer collaborative product innovation (CCPI) has become a new worldwide product design trend. Based on this popularity, we introduced CCPI into the low-carbon product design process. An essential step for implementi...

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Bibliographic Details
Published in:International journal of environmental research and public health 2018-07, Vol.15 (7), p.1446
Main Authors: Liu, Aijun, Zhu, Qiuyun, Ji, Xiaohui, Lu, Hui, Tsai, Sang-Bing, Wang, Jiangtao, Liang, Biru
Format: Article
Language:English
Subjects:
Algorithms
Carbon dioxide
Carbon Footprint
Climate change
Collaboration
Consumer Behavior
Cooperative Behavior
Customer satisfaction
Customers
Data envelopment analysis
Design
Design specifications
Emissions
Environmental impact
Equipment Design - methods
Fuzzy Logic
Game theory
Genetic algorithms
Genetic analysis
Greenhouse effect
Greenhouse gases
Heterogeneity
Humans
Industrial design
Industrialized nations
Innovations
Linear programming
Liquid crystal displays
Mathematical models
Methods
Optimization
Perception
Preferences
Product design
Product development
Public health
Social Behavior
System theory
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Summary:Low-carbon product design is an important way to reduce greenhouse gas emission. Customer collaborative product innovation (CCPI) has become a new worldwide product design trend. Based on this popularity, we introduced CCPI into the low-carbon product design process. An essential step for implementing low carbon CCPI is to clarify key low carbon requirements of customers. This study tested a novel method for perceiving key requirements of customer collaboration low-carbon product design based on fuzzy grey relational analysis and genetic algorithm. Firstly, the study considered consumer heterogeneity, allowing different types of customers to evaluate low carbon requirements in appropriate formats that reflected their degrees of uncertainty. Then, a nonlinear optimization model was proposed to establish the information aggregation factor of customers based on the genetic algorithm. The weight of customers was obtained simultaneously. Next, the key low carbon requirements of customer were identified. Finally, the effectiveness of the proposed method was illustrated with a case related to a low carbon liquid crystal display.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph15071446