Multi-objective Discrete Brainstorming Optimizer for Stochastic Disassembly Line Balancing Problem Subject to Disassembly Failure

Kun Wu; Xi Wang Guo; Meng Chu Zhou; Shi Xin Liu; Liang Qi

doi:10.1109/SMC42975.2020.9282908

Multi-objective Discrete Brainstorming Optimizer for Stochastic Disassembly Line Balancing Problem Subject to Disassembly Failure

Kun Wu, Xi Wang Guo, Meng Chu Zhou, Shi Xin Liu, Liang Qi

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

A disassembly line balancing problem (DLBP) exists in the recycling process of end-of-life (EOL) products. It involves such factors as uncertainty of disassembly time and disassembly failure risk. Effective decisions can be made by taking them into full consideration. Under the constraints of disassembly precedence relationships and cycle time, this work establishes a stochastic multi-objective DLBP model subject to disassembly failure based on a disassembly AND/OR graph of EOL products. It considers disassembly failure risk and comprehensively evaluates the profit, energy consumption, average idle time of workstations, and hazard disassembly. Then, a new multi-objective discrete brainstorming optimizer that combines stochastic simulation is proposed for obtaining high- quality feasible solutions. Experimental results show the validity of the proposed algorithm. It outperforms both nondominated sorting genetic algorithm II and multi-objective discrete grey wolf optimizer.

Original language	English (US)
Title of host publication	2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1224-1229
Number of pages	6
Volume	2020-October
ISBN (Electronic)	9781728185262
DOIs	https://doi.org/10.1109/SMC42975.2020.9282908
State	Published - Oct 11 2020
Event	2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada Duration: Oct 11 2020 → Oct 14 2020

Conference

Conference	2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
Country/Territory	Canada
City	Toronto
Period	10/11/20 → 10/14/20

All Science Journal Classification (ASJC) codes

Software
Control and Systems Engineering
Human-Computer Interaction
Computer Science Applications
Electrical and Electronic Engineering

Keywords

Disassembly line balancing problem
disassembly failure
energy consumption
genetic algorithm
grey wolf optimizer
multi-objective discrete brainstorming optimizer
stochastic simulation

Access to Document

10.1109/SMC42975.2020.9282908

Cite this

Wu, K., Guo, X. W., Zhou, M. C., Liu, S. X., & Qi, L. (2020). Multi-objective Discrete Brainstorming Optimizer for Stochastic Disassembly Line Balancing Problem Subject to Disassembly Failure. In 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 (Vol. 2020-October, pp. 1224-1229). [9282908] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC42975.2020.9282908

Wu, Kun ; Guo, Xi Wang ; Zhou, Meng Chu ; Liu, Shi Xin ; Qi, Liang. / Multi-objective Discrete Brainstorming Optimizer for Stochastic Disassembly Line Balancing Problem Subject to Disassembly Failure. 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020. Vol. 2020-October Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1224-1229

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abstract = "A disassembly line balancing problem (DLBP) exists in the recycling process of end-of-life (EOL) products. It involves such factors as uncertainty of disassembly time and disassembly failure risk. Effective decisions can be made by taking them into full consideration. Under the constraints of disassembly precedence relationships and cycle time, this work establishes a stochastic multi-objective DLBP model subject to disassembly failure based on a disassembly AND/OR graph of EOL products. It considers disassembly failure risk and comprehensively evaluates the profit, energy consumption, average idle time of workstations, and hazard disassembly. Then, a new multi-objective discrete brainstorming optimizer that combines stochastic simulation is proposed for obtaining high- quality feasible solutions. Experimental results show the validity of the proposed algorithm. It outperforms both nondominated sorting genetic algorithm II and multi-objective discrete grey wolf optimizer.",

keywords = "Disassembly line balancing problem, disassembly failure, energy consumption, genetic algorithm, grey wolf optimizer, multi-objective discrete brainstorming optimizer, stochastic simulation",

author = "Kun Wu and Guo, {Xi Wang} and Zhou, {Meng Chu} and Liu, {Shi Xin} and Liang Qi",

note = "Funding Information: This work is supported in part by Liaoning Province Education Department Scientific Research Foundation of China under Grant No. L2019027; LiaoNing Revitalization Talents Program under Grant No. XLYC1907166; The Natural Science Foundation of Shandong Province under Grant ZR2019BF004; National Natural Science Foundation of China (61573089). Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 ; Conference date: 11-10-2020 Through 14-10-2020",

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Wu, K, Guo, XW, Zhou, MC, Liu, SX & Qi, L 2020, Multi-objective Discrete Brainstorming Optimizer for Stochastic Disassembly Line Balancing Problem Subject to Disassembly Failure. in 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020. vol. 2020-October, 9282908, Institute of Electrical and Electronics Engineers Inc., pp. 1224-1229, 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020, Toronto, Canada, 10/11/20. https://doi.org/10.1109/SMC42975.2020.9282908

Multi-objective Discrete Brainstorming Optimizer for Stochastic Disassembly Line Balancing Problem Subject to Disassembly Failure. / Wu, Kun; Guo, Xi Wang; Zhou, Meng Chu; Liu, Shi Xin; Qi, Liang.

2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020. Vol. 2020-October Institute of Electrical and Electronics Engineers Inc., 2020. p. 1224-1229 9282908.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Wu, Kun

AU - Guo, Xi Wang

AU - Zhou, Meng Chu

AU - Liu, Shi Xin

AU - Qi, Liang

N1 - Funding Information: This work is supported in part by Liaoning Province Education Department Scientific Research Foundation of China under Grant No. L2019027; LiaoNing Revitalization Talents Program under Grant No. XLYC1907166; The Natural Science Foundation of Shandong Province under Grant ZR2019BF004; National Natural Science Foundation of China (61573089). Publisher Copyright: © 2020 IEEE.

PY - 2020/10/11

Y1 - 2020/10/11

N2 - A disassembly line balancing problem (DLBP) exists in the recycling process of end-of-life (EOL) products. It involves such factors as uncertainty of disassembly time and disassembly failure risk. Effective decisions can be made by taking them into full consideration. Under the constraints of disassembly precedence relationships and cycle time, this work establishes a stochastic multi-objective DLBP model subject to disassembly failure based on a disassembly AND/OR graph of EOL products. It considers disassembly failure risk and comprehensively evaluates the profit, energy consumption, average idle time of workstations, and hazard disassembly. Then, a new multi-objective discrete brainstorming optimizer that combines stochastic simulation is proposed for obtaining high- quality feasible solutions. Experimental results show the validity of the proposed algorithm. It outperforms both nondominated sorting genetic algorithm II and multi-objective discrete grey wolf optimizer.

AB - A disassembly line balancing problem (DLBP) exists in the recycling process of end-of-life (EOL) products. It involves such factors as uncertainty of disassembly time and disassembly failure risk. Effective decisions can be made by taking them into full consideration. Under the constraints of disassembly precedence relationships and cycle time, this work establishes a stochastic multi-objective DLBP model subject to disassembly failure based on a disassembly AND/OR graph of EOL products. It considers disassembly failure risk and comprehensively evaluates the profit, energy consumption, average idle time of workstations, and hazard disassembly. Then, a new multi-objective discrete brainstorming optimizer that combines stochastic simulation is proposed for obtaining high- quality feasible solutions. Experimental results show the validity of the proposed algorithm. It outperforms both nondominated sorting genetic algorithm II and multi-objective discrete grey wolf optimizer.

KW - Disassembly line balancing problem

KW - disassembly failure

KW - energy consumption

KW - genetic algorithm

KW - grey wolf optimizer

KW - multi-objective discrete brainstorming optimizer

KW - stochastic simulation

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M3 - Conference contribution

AN - SCOPUS:85098852811

VL - 2020-October

SP - 1224

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BT - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020

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ER -

Wu K, Guo XW, Zhou MC, Liu SX, Qi L. Multi-objective Discrete Brainstorming Optimizer for Stochastic Disassembly Line Balancing Problem Subject to Disassembly Failure. In 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020. Vol. 2020-October. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1224-1229. 9282908 https://doi.org/10.1109/SMC42975.2020.9282908

Multi-objective Discrete Brainstorming Optimizer for Stochastic Disassembly Line Balancing Problem Subject to Disassembly Failure

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