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Flexible Job-Shop Scheduling for Reduced Manufacturing Carbon Footprint

[+] Author Affiliations
Qiong Liu, Youquan Tian, Freddy O. Chekem

Huazhong University of Science and Technology, Wuhan, China

Chao Wang

Beijing Jiaotong University, Beijing, China

John W. Sutherland

Purdue University, West Lafayette, IN

Paper No. MSEC2017-2630, pp. V004T05A023; 13 pages
doi:10.1115/MSEC2017-2630
From:
  • ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing
  • Volume 4: Bio and Sustainable Manufacturing
  • Los Angeles, California, USA, June 4–8, 2017
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5075-6
  • Copyright © 2017 by ASME

abstract

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.

Copyright © 2017 by ASME

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