TY - JOUR
T1 - Multiresource-Constrained Selective Disassembly with Maximal Profit and Minimal Energy Consumption
AU - Guo, Xiwang
AU - Zhou, Mengchu
AU - Liu, Shixin
AU - Qi, Liang
N1 - Funding Information:
Manuscript received October 30, 2019; revised December 31, 2019 and March 6, 2020; accepted April 28, 2020. Date of publication June 19, 2020; date of current version April 7, 2021. This article was recommended for publication by Associate Editor C. K. Ahn and Editor Y. Tang upon evaluation of the reviewers’ comments. This work was supported in part by NSFC under Grant 61573089, Grant 51405075, and Grant 61903229, in part by the Liaoning Province Education Department Scientific Research Foundation of China under Grant L2019027, in part by the Liaoning Province Dr. Research Foundation of China under Grant 20170520135, in part by the LiaoNing Revitalization Talents Program under Grant XLYC1907166, in part by the Natural Science Foundation of Shandong Province under Grant ZR2019BF004, and in part by The Deanship of Scientific Research (DSR) at King Abdulaziz University under Grant RG-21-135-38. (Corresponding authors: MengChu Zhou; Liang Qi.) Xiwang Guo is with the Computer and Communication Engineering College, Liaoning Shihua University, Fushun 113001, China, and also with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102 USA (e-mail: x.w.guo@163.com).
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2021/4
Y1 - 2021/4
N2 - Industrial products' reuse, recovery, and recycling are very important due to the exhaustion of ecological resources. Effective product disassembly planning methods can improve the recovery efficiency and reduce harmful impact on the environment. However, the existing approaches pay little attention to disassembly resources, such as tools and operators that can significantly influence the optimal disassembly sequences. This article considers a multiobjective resource-constrained disassembly optimization problem modeled with timed Petri nets such that energy consumption is minimized, while disassembly profit is maximized. Since its solution complexity has exponential growth with the number of components in a product, a multiobjective genetic algorithm based on an external archive is used to solve it. Its effectiveness is verified by comparing it with nondominated sorting genetic algorithm II and a collaborative resource allocation strategy for a multiobjective evolutionary algorithm based on decomposition. Note to Practitioners-This article establishes a novel dual-objective optimization model for product disassembly subject to multiresource constraints. In an actual disassembly process, a decision-maker may want to minimize energy consumption and maximize disassembly profit. This article considers both objectives and proposes a multiobjective genetic algorithm based on an external archive to solve optimal disassembly problems. The experimental results show that the proposed approach can solve them effectively. The obtained solutions give decision-makers multiple choices to select the right disassembly process when an actual product is disassembled.
AB - Industrial products' reuse, recovery, and recycling are very important due to the exhaustion of ecological resources. Effective product disassembly planning methods can improve the recovery efficiency and reduce harmful impact on the environment. However, the existing approaches pay little attention to disassembly resources, such as tools and operators that can significantly influence the optimal disassembly sequences. This article considers a multiobjective resource-constrained disassembly optimization problem modeled with timed Petri nets such that energy consumption is minimized, while disassembly profit is maximized. Since its solution complexity has exponential growth with the number of components in a product, a multiobjective genetic algorithm based on an external archive is used to solve it. Its effectiveness is verified by comparing it with nondominated sorting genetic algorithm II and a collaborative resource allocation strategy for a multiobjective evolutionary algorithm based on decomposition. Note to Practitioners-This article establishes a novel dual-objective optimization model for product disassembly subject to multiresource constraints. In an actual disassembly process, a decision-maker may want to minimize energy consumption and maximize disassembly profit. This article considers both objectives and proposes a multiobjective genetic algorithm based on an external archive to solve optimal disassembly problems. The experimental results show that the proposed approach can solve them effectively. The obtained solutions give decision-makers multiple choices to select the right disassembly process when an actual product is disassembled.
KW - Disassembly sequence
KW - Petri nets (PNs)
KW - intelligent algorithm
KW - multiobjective
KW - multiresource constraints
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U2 - 10.1109/TASE.2020.2992220
DO - 10.1109/TASE.2020.2992220
M3 - Article
AN - SCOPUS:85104088991
SN - 1545-5955
VL - 18
SP - 804
EP - 816
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
IS - 2
M1 - 9121663
ER -