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research-article

Flexible Job-Shop Scheduling for Reduced Manufacturing Carbon Footprint

[+] Author and Article Information
Qiong Liu

State Key Laboratory of Digital Manufacturing, Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
qiongliu@hust.edu.cn

Youquan Tian

State Key Laboratory of Digital Manufacturing, Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
372039258@qq.com

Chao Wang

School of Economics and Management, Beijing Jiaotong University, Beijing, P.R. China
chaowanghn@vip.163.com

Freddy O. Chekem

State Key Laboratory of Digital Manufacturing, Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
1327991401@qq.com

John Sutherland

Environmental and Ecological Engineering, Purdue University, West Lafayette, Indiana, USA
jwsuther@purdue.edu

1Corresponding author.

ASME doi:10.1115/1.4037710 History: Received July 19, 2017; Revised August 14, 2017

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 (c) 2017 by ASME
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