TY - JOUR
T1 - A Multiobjective Disassembly Planning for Value Recovery and Energy Conservation from End-of-Life Products
AU - Ren, Yaping
AU - Jin, Hongyue
AU - Zhao, Fu
AU - Qu, Ting
AU - Meng, Leilei
AU - Zhang, Chaoyong
AU - Zhang, Biao
AU - Wang, Geng
AU - Sutherland, John W.
N1 - Funding Information:
Manuscript received October 20, 2019; revised December 31, 2019 and March 16, 2020; accepted March 25, 2020. Date of publication June 2, 2020; date of current version April 7, 2021. This article was recommended for publication by Editor Y. Tang upon evaluation of the reviewers’ comments. This work was supported in part by the Funds for the Basic and Applied Basic Research Foundation of Guangdong Province of China under Grant 2019A1515110399, in part by the National Natural Science Foundation of China under Grant 51875251, in part by the Key Research and Development Program of Guangdong Province under Grant 2019B090921001, in part by the Guangdong Special Support Talent Program—Innovation and Entrepreneurship Leading Team under Grant 2019BT02S593, in part by the 2018 Guangzhou Leading Innovation Team Program (China) under Grant 201909010006, in part by the Blue Fire Project (Huizhou) Industry-University-Research Joint Innovation Fund of the Ministry of Education (China) under Grant CXZJHZ201722, and in part by the Fundamental Research Funds for the Central Universities under Grant 11618401. (Corresponding authors: Leilei Meng; Chaoyong Zhang.) Yaping Ren and Ting Qu are with the School of Intelligent Systems Science and Engineering, Institute of Physical Internet, Jinan University (Zhuhai Campus), Zhuhai 519070, China (e-mail: renyp1@163.com; quting@jnu.edu.cn).
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2021/4
Y1 - 2021/4
N2 - Demanufacturing aims to recover value and conserve energy from end-of-life (EOL) products, contributing to sustainable manufacturing. To make the full use of EOL products, they are usually disassembled into components that have different values and embodied energy at different EOL options. This article studies a disassembly planning (DP) that integrates the decisions on disassembly sequence and EOL strategy to maximize the recovered value and energy conservation from EOL products. We propose a multiobjective DP based on the value recovery and energy conservation (MDPVE) model, which is different from the existing DP models by focusing on the embodied energy rather than the energy consumption during disassembly. An adapted multiobjective artificial bee colony (ABC) algorithm [multiobjective ABC (MOABC)] is developed to identify the Pareto solutions for the MDPVE and is compared with a well-known metaheuristic algorithm, Non-dominated Sorting Genetic Algorithm-II (NSGA-II). A real-world case study demonstrated the superior solution quality and computational efficiency of MOABC. Note to Practitioners-There is often more than one treatment option for EOL products or components, including reuse, remanufacturing, and recycling. However, the decision on which EOL option to select is not considered in most of the DP studies by assuming an EOL option given for each component. Hence, the disassembly plan with the EOL decision is focused in this article. As energy sustainability gains an increasing attention, it is essential to assess the profitability and energy conservation simultaneously for EOL products. Since there could be a tradeoff between recovered profit and conserved energy, a multiobjective evolutionary algorithm is developed for generating Pareto solutions which help decision-makers to find good solutions for both evaluation indicators.
AB - Demanufacturing aims to recover value and conserve energy from end-of-life (EOL) products, contributing to sustainable manufacturing. To make the full use of EOL products, they are usually disassembled into components that have different values and embodied energy at different EOL options. This article studies a disassembly planning (DP) that integrates the decisions on disassembly sequence and EOL strategy to maximize the recovered value and energy conservation from EOL products. We propose a multiobjective DP based on the value recovery and energy conservation (MDPVE) model, which is different from the existing DP models by focusing on the embodied energy rather than the energy consumption during disassembly. An adapted multiobjective artificial bee colony (ABC) algorithm [multiobjective ABC (MOABC)] is developed to identify the Pareto solutions for the MDPVE and is compared with a well-known metaheuristic algorithm, Non-dominated Sorting Genetic Algorithm-II (NSGA-II). A real-world case study demonstrated the superior solution quality and computational efficiency of MOABC. Note to Practitioners-There is often more than one treatment option for EOL products or components, including reuse, remanufacturing, and recycling. However, the decision on which EOL option to select is not considered in most of the DP studies by assuming an EOL option given for each component. Hence, the disassembly plan with the EOL decision is focused in this article. As energy sustainability gains an increasing attention, it is essential to assess the profitability and energy conservation simultaneously for EOL products. Since there could be a tradeoff between recovered profit and conserved energy, a multiobjective evolutionary algorithm is developed for generating Pareto solutions which help decision-makers to find good solutions for both evaluation indicators.
KW - Demanufacturing
KW - disassembly planning (DP)
KW - end-of-life (EOL) decision
KW - energy conservation
KW - multiobjective optimization
KW - value recovery
UR - http://www.scopus.com/inward/record.url?scp=85102799610&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102799610&partnerID=8YFLogxK
U2 - 10.1109/TASE.2020.2987391
DO - 10.1109/TASE.2020.2987391
M3 - Article
AN - SCOPUS:85102799610
SN - 1545-5955
VL - 18
SP - 791
EP - 803
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
IS - 2
M1 - 9106832
ER -