FLEXIBLE JOB-SHOP SCHEDULING FOR REDUCED MANUFACTURING CARBON FOOTPRINT

被引：0
|
作者：
Liu, Qiong [1 ]
Tian, Youquan [1 ]
Wang, Chao [2 ]
Chekem, Freddy [1 ]
Sutherland, John W. [3 ]
机构：
[1] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Sch Mech Sci & Engn, Wuhan, Hubei, Peoples R China
[2] Beijing Jiaotong Univ, Sch Econ & Management, Beijing, Peoples R China
[3] Purdue Univ, Environm & Ecol Engn, W Lafayette, IN 47907 USA
来源：
PROCEEDINGS OF THE ASME 12TH INTERNATIONAL MANUFACTURING SCIENCE AND ENGINEERING CONFERENCE - 2017, VOL 4 | 2017年
基金：
中国国家自然科学基金;
关键词：
Flexible Job-shop Scheduling; Carbon Footprint; Carbon Emission; Non-Dominated Sorting Genetic Algorithm; GENETIC ALGORITHM; CONSUMPTION;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
In order to help manufacturing companies quantify and reduce product carbon footprints in a mixed model manufacturing system, a product carbon footprint oriented multi-objective flexible job-shop scheduling optimization model is proposed. The production portion of the product carbon footprint, based on the mapping relations between products and the carbon emissions within the manufacturing system, is proposed to calculate the product carbon footprint in the mixed model manufacturing system. Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to solve the proposed model. In order to help decision makers to choose the most suitable solution from the Pareto set as its execution solution, a method based on grades of product carbon footprints is proposed. Finally, the efficacy of the proposed model and algorithm are examined via a case study.
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收藏
页数：13
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