A decision-making methodology for low-carbon electronic product design
作者:
Highlights:
• An estimation model of GHG emissions is developed to predict the carbon footprint.
• This paper can find benchmark low-carbon design parts to acquire information about how to enhance the low-carbon design.
• Engineers can understand the causes of the poor carbon footprint (CF) performance.
• The analytical results can help engineers propose low-carbon design alternatives.
• This study can find the optimal low-carbon design alternative combination.
摘要
Degradation of environment can significantly affect human existence and development. Therefore, environmental protection deserves significant attention. Low-carbon products have become an attractive option due to global warming. To efficiently and effectively achieve the aim of eco-friendly electronic product design, this paper develops a decision-making methodology for low-carbon electronic product design. First, the proposed methodology estimates the released amounts of greenhouse gases for different product designs throughout the major phases of a product's life cycle. This research can subsequently determine the benchmarking low-carbon design parts (as compared with other competitive products) and discern the causes of the poor carbon footprint (CF) performance. This will stimulate innovation to propose the low-carbon design alternatives. This research creates an evaluation model of low-carbon design alternative combinations in order to assess their performance. A multi-objective genetic algorithm is employed to determine an optimal low-carbon design alternative combination satisfying the CF constraint of a new product and minimizing the design time and cost. Finally, this research utilizes an MP3 player as a case study to showcase the significant efficacy of the proposed methodology.
论文关键词:Carbon footprint,Low-carbon design alternative,Electronic product design,Design time and cost
论文评审过程:Received 3 April 2013, Revised 20 November 2014, Accepted 4 January 2015, Available online 12 January 2015.
论文官网地址:https://doi.org/10.1016/j.dss.2015.01.004