EconPapers    
Economics at your fingertips  
 

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

Aijun Liu, Qiuyun Zhu, Xiaohui Ji, Hui Lu and Sang-Bing Tsai
Additional contact information
Aijun Liu: Department of Management Engineering, School of Economics & Management, Xidian University, Xi’an 710071, China
Qiuyun Zhu: Department of Management Engineering, School of Economics & Management, Xidian University, Xi’an 710071, China
Xiaohui Ji: Department of Management Engineering, School of Economics & Management, Xidian University, Xi’an 710071, China
Hui Lu: Tianhua College, Shanghai Normal University, Shanghai 201815, China
Sang-Bing Tsai: Zhongshan Institute, University of Electronic Science and Technology, Zhongshan 528400, China

IJERPH, 2018, vol. 15, issue 7, 1-32

Abstract: 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.

Keywords: low-carbon product design; customer collaborative product innovation; fuzzy grey relational analysis; genetic algorithm; green operation; green service; sustainability (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1660-4601/15/7/1446/pdf (application/pdf)
https://www.mdpi.com/1660-4601/15/7/1446/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:15:y:2018:i:7:p:1446-:d:157033

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-03-19
Handle: RePEc:gam:jijerp:v:15:y:2018:i:7:p:1446-:d:157033