Optimization of the Disassembly Sequencing Problem on the Basis of Self-Adaptive Simplified Swarm Optimization

Yeh, W

Optimization of the Disassembly Sequencing Problem on the Basis of Self-Adaptive Simplified Swarm Optimization

Yeh, W

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Publisher:: IEEE
Publication Type:: Journal Article
Citation:: IEEE Transactions On Systems Man And Cybernetics Part A-Systems And Humans, 2012, 42 (1), pp. 250 - 261
Issue Date:: 2012-01

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Field	Value	Language
dc.contributor.author	Yeh, W	en_US
dc.date.issued	2012-01	en_US
dc.identifier.citation	IEEE Transactions On Systems Man And Cybernetics Part A-Systems And Humans, 2012, 42 (1), pp. 250 - 261	en_US
dc.identifier.issn	1083-4427	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22208
dc.description.abstract	The end-of-life (EOL) disassembly sequencing problem (DSP) has become increasingly important in the process of handling EOL products. This paper proposes a solution procedure for the âEOL DSPâ ; the procedure is based on a novel soft-computing algorithm that utilizes modified âsimplified swarm optimization,â and the procedure combines the precedence preservative operator, feasible solution generator, self-adaptive parameter control, and repetitive pairwise exchange procedures. By taking into consideration the non-deterministic polynomial time (NP)-complete nature of the problem, the proposed algorithm efficiently seeks the optimal disassembly sequence with a novel approach; this approach involves reducing the initial solution space and using a combination of soft-computing algorithms for achieving higher computational efficiency and solution quality. The results presented in this paper show that the proposed algorithm outperforms the existing algorithms in terms of solution quality achieved in a limited computation time.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	IEEE Transactions On Systems Man And Cybernetics Part A-Systems And Humans	en_US
dc.relation.isbasedon	10.1109/TSMCA.2011.2157135	en_US
dc.rights	© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Optimization of the Disassembly Sequencing Problem on the Basis of Self-Adaptive Simplified Swarm Optimization	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	42	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.issue	1	en_US
pubs.volume	42	en_US

Abstract:

The end-of-life (EOL) disassembly sequencing problem (DSP) has become increasingly important in the process of handling EOL products. This paper proposes a solution procedure for the âEOL DSPâ ; the procedure is based on a novel soft-computing algorithm that utilizes modified âsimplified swarm optimization,â and the procedure combines the precedence preservative operator, feasible solution generator, self-adaptive parameter control, and repetitive pairwise exchange procedures. By taking into consideration the non-deterministic polynomial time (NP)-complete nature of the problem, the proposed algorithm efficiently seeks the optimal disassembly sequence with a novel approach; this approach involves reducing the initial solution space and using a combination of soft-computing algorithms for achieving higher computational efficiency and solution quality. The results presented in this paper show that the proposed algorithm outperforms the existing algorithms in terms of solution quality achieved in a limited computation time.

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http://hdl.handle.net/10453/22208