Grey Wolf optimizer adapted for disassembly sequencing problems

Matthew Chen, Mengchu Zhou, Xiwang Guo, Xiaoyu Sean Lu, Jingchu Ji, Ziyan Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

One of important industrial problems is related to the recovery, reuse, and disposal of products at their end-of-life (EOL). There are a vast number of methods to perform such operations; however, each method is associated with different cost. To harvest materials from an EOL product while remaining environmentally conscious, an optimal, or at least near-optimal, disassembly sequence is necessary to ensure the efficiency, sustainability, and economic viability of a remanufacturing plant. Because the number of possible sequences increases factorially, a heuristic algorithm is an effective time-efficient method for finding a near-optimal sequence. This paper explores a recent novel optimization algorithm named Grey Wolf Optimizer (GWO) and its application to the disassembly sequencing problem. Since GWO is an algorithm designed for continuous optimization problems, it cannot be directly applied to a disassembly sequencing problem that is discrete. Therefore, keeping the main ideas of GWO intact, a Sequencing GWO (SGWO) is proposed for the first time to specifically solve this problem. Experimental results have shown that SGWO performs better than some existing algorithms, such as Genetic Algorithm and a Teacher-Learner-based Optimizer Algorithm previously used to solve this problem.

Original languageEnglish (US)
Title of host publicationProceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019
EditorsHaibin Zhu, Jiacun Wang, MengChu Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages46-51
Number of pages6
ISBN (Electronic)9781728100838
DOIs
StatePublished - May 1 2019
Event16th IEEE International Conference on Networking, Sensing and Control, ICNSC 2019 - Banff, Canada
Duration: May 9 2019May 11 2019

Publication series

NameProceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019

Conference

Conference16th IEEE International Conference on Networking, Sensing and Control, ICNSC 2019
CountryCanada
CityBanff
Period5/9/195/11/19

Fingerprint

Disassembly
sequencing
wolves
Sequencing
Remanufacturing
Heuristic algorithms
optimization
Continuous Optimization
instructors
reuse
disposal
Sustainability
Sustainable development
products
Viability
viability
genetic algorithms
Genetic algorithms
Heuristic algorithm
Reuse

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Optimization
  • Instrumentation
  • Computer Networks and Communications

Keywords

  • Disassembly Sequencing
  • End-of-life (EOF) products
  • Grey Wolf Optimizer
  • Optimization Algorithm
  • Swarm Intelligence

Cite this

Chen, M., Zhou, M., Guo, X., Lu, X. S., Ji, J., & Zhao, Z. (2019). Grey Wolf optimizer adapted for disassembly sequencing problems. In H. Zhu, J. Wang, & M. Zhou (Eds.), Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019 (pp. 46-51). [8743232] (Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICNSC.2019.8743232
Chen, Matthew ; Zhou, Mengchu ; Guo, Xiwang ; Lu, Xiaoyu Sean ; Ji, Jingchu ; Zhao, Ziyan. / Grey Wolf optimizer adapted for disassembly sequencing problems. Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019. editor / Haibin Zhu ; Jiacun Wang ; MengChu Zhou. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 46-51 (Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019).
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title = "Grey Wolf optimizer adapted for disassembly sequencing problems",
abstract = "One of important industrial problems is related to the recovery, reuse, and disposal of products at their end-of-life (EOL). There are a vast number of methods to perform such operations; however, each method is associated with different cost. To harvest materials from an EOL product while remaining environmentally conscious, an optimal, or at least near-optimal, disassembly sequence is necessary to ensure the efficiency, sustainability, and economic viability of a remanufacturing plant. Because the number of possible sequences increases factorially, a heuristic algorithm is an effective time-efficient method for finding a near-optimal sequence. This paper explores a recent novel optimization algorithm named Grey Wolf Optimizer (GWO) and its application to the disassembly sequencing problem. Since GWO is an algorithm designed for continuous optimization problems, it cannot be directly applied to a disassembly sequencing problem that is discrete. Therefore, keeping the main ideas of GWO intact, a Sequencing GWO (SGWO) is proposed for the first time to specifically solve this problem. Experimental results have shown that SGWO performs better than some existing algorithms, such as Genetic Algorithm and a Teacher-Learner-based Optimizer Algorithm previously used to solve this problem.",
keywords = "Disassembly Sequencing, End-of-life (EOF) products, Grey Wolf Optimizer, Optimization Algorithm, Swarm Intelligence",
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Chen, M, Zhou, M, Guo, X, Lu, XS, Ji, J & Zhao, Z 2019, Grey Wolf optimizer adapted for disassembly sequencing problems. in H Zhu, J Wang & M Zhou (eds), Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019., 8743232, Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 46-51, 16th IEEE International Conference on Networking, Sensing and Control, ICNSC 2019, Banff, Canada, 5/9/19. https://doi.org/10.1109/ICNSC.2019.8743232

Grey Wolf optimizer adapted for disassembly sequencing problems. / Chen, Matthew; Zhou, Mengchu; Guo, Xiwang; Lu, Xiaoyu Sean; Ji, Jingchu; Zhao, Ziyan.

Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019. ed. / Haibin Zhu; Jiacun Wang; MengChu Zhou. Institute of Electrical and Electronics Engineers Inc., 2019. p. 46-51 8743232 (Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Zhou, Mengchu

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Chen M, Zhou M, Guo X, Lu XS, Ji J, Zhao Z. Grey Wolf optimizer adapted for disassembly sequencing problems. In Zhu H, Wang J, Zhou M, editors, Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 46-51. 8743232. (Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019). https://doi.org/10.1109/ICNSC.2019.8743232