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Multi-objective co-operative co-evolutionary algorithm for minimizing carbon footprint and maximizing line efficiency in robotic assembly line systems

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journal contribution
posted on 16.05.2019, 09:14 by J. Mukund Nilakantan, Zixiang Li, Qiuhua Tang, Peter Nielsen
Methods for reducing the carbon footprint is receiving increasing attention from industry as they work to create sustainable products. Assembly line systems are widely utilized to assemble different types of products and in recent years, robots have become extensively utilized, replacing manual labor. This paper focuses on minimizing the carbon footprint for robotic assembly line systems, a topic that has received limited attention in academia. This paper is primarily focused on developing a mathematical model to simultaneously minimize the total carbon footprint and maximize the efficiency of robotic assembly line systems. Due to the NP-hard nature of the considered problem, a multi-objective co-operative co-evolutionary (MOCC) algorithm is developed to solve it. Several improvements are applied to enhance the performance of the MOCC for obtaining a strong local search capacity and faster search speed. The performance of the proposed MOCC algorithm is compared with three other high-performing multi-objective methods. Computational and statistical results from the set of benchmark problems show that the proposed model can reduce the carbon footprint effectively. The proposed MOCC outperforms the other three methods by a significant margin as shown by utilizing one graphical and two quantitative Pareto compliant indicators.

Funding

This research work is funded by the National Natural Science Foundation of China (Grant No. 51275366) (Qiuhua Tang).

History

Citation

Journal of Cleaner Production, 2017, 156, pp. 124-136

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Engineering

Version

AM (Accepted Manuscript)

Published in

Journal of Cleaner Production

Publisher

Elsevier

issn

0959-6526

Acceptance date

05/04/2017

Copyright date

2017

Available date

16/05/2019

Publisher version

https://www.sciencedirect.com/science/article/pii/S0959652617307394?via=ihub

Language

en