Department of Management Engineering, School of Economics & Management, Xidian University, Xi'an 710071, China.
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Int J Environ Res Public Health. 2018 Jul 9;15(7):1446. doi: 10.3390/ijerph15071446.
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.
低碳产品设计是减少温室气体排放的重要途径。客户协同产品创新（CCPI）已成为一种新的全球性产品设计趋势。基于这一流行趋势，我们将 CCPI 引入低碳产品设计过程中。实施低碳 CCPI 的一个重要步骤是明确客户的关键低碳要求。本研究测试了一种基于模糊灰色关联分析和遗传算法感知客户协同低碳产品设计关键要求的新方法。首先，该研究考虑了消费者的异质性，允许不同类型的客户以反映其不确定性程度的适当格式评估低碳要求。然后，提出了一个非线性优化模型，基于遗传算法建立客户的信息聚合因子。同时获得客户的权重。接下来，确定客户的关键低碳要求。最后，通过一个与低碳液晶显示器相关的案例说明了所提出方法的有效性。
