Skip to main content
Springer Nature Link
Log in

A disassembly sequence planning method with improved discrete grey wolf optimizer for equipment maintenance in hydropower station

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The foundation of hydropower station equipment maintenance is parts disassembly, thus a reasonable disassembly sequence can optimize the maintenance efficiency. To this end, a disassembly sequence planning method based on improved discrete grey wolf optimizer (IDGWO) is proposed in this paper. Firstly, in the modeling, a directed graph with combination nodes is adopted to represent the priority constraint relationship of parts. In addition, a sequence evaluation index based on operator moving distance is added to the fitness function. Subsequently, in algorithm design, we improve the optimization mechanism of traditional grey wolf optimizer and propose a self-renewal (SR) mechanism and an exchange optimization operator (EOO) to enhance the optimization efficiency and stability. Finally, two experiments are conducted using five actual maintenance items. The first experiment is performed to verify the effectiveness of the proposed SR mechanism and EOO. The second experiment is adopted to verify the superiority of the proposed IDGWO compared with four well-known algorithms. The experimental results show that in five actual maintenance items, the proportion of the optimal sequence found by the IDGWO reach to 100%, 32%, 29%, 100% and 100%, respectively, which is higher than comparison algorithms. In addition, IDGWO has a prominent performance in stability and convergence speed than other comparison algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author, [Xing Liu], upon reasonable request.

References

  1. Bierwirth C, Mattfeld DC, Kopfer H (1996) On permutation representations for scheduling problems. In: International Conference on Parallel Problem Solving from Nature. Springer, pp. 310–318

  2. Chunming Z (2016) Optimization for disassemble sequence planning of electromechanical products during recycling process based on genetic algorithms. Int J Multimed Ubiquitous Eng 11(4):107–114

    Article  Google Scholar 

  3. Dong T, Ling Z, Tong R et al (2006) A hierarchical approach to disassembly sequence planning for mechanical product. Int J Adv Manuf Technol 30(5–6):507–520

    Article  Google Scholar 

  4. Edwin Dhas P, Sankara Gomathi B (2020) A novel clustering algorithm by clubbing ghfcm and gwo for microarray gene data. J Supercomput 76(8):5679–5693

    Article  Google Scholar 

  5. Feng Y, Zhou MC, Tian G, et al (2018) Target disassembly sequencing and scheme evaluation for cnc machine tools using improved multiobjective ant colony algorithm and fuzzy integral. In: IEEE transactions on systems, man, and cybernetics: systems. pp. 1–14

  6. Gungor A, Gupta SM (2001) Disassembly sequence plan generation using a branch-and-bound algorithm. Int J Prod Res 39(3):481–509

    Article  Google Scholar 

  7. Kanehara T, Suzuki T, Inaba A, et al (1993) On algebraic and graph structural properties of assembly petri net-searching by linear programming. In: Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’93). IEEE, pp 2286–2293

  8. Kang JG, Lee DH, Xirouchakis P et al (2001) Parallel disassembly sequencing with sequence-dependent operation times. CIRP Ann Manuf Technol 50(1):343–346

    Article  Google Scholar 

  9. Kheder M, Trigui M, Aifaoui N (2017a) A disassembly sequence planning approach for maintenance. In: Advances on mechanics, design engineering and manufacturing. Springer, p 81–89

  10. Kheder M, Trigui M, Aifaoui N (2017) Optimization of disassembly sequence planning for preventive maintenance. Int J Adv Manuf Technol 90(5):1337–1349

    Article  Google Scholar 

  11. Kongar E, Gupta SM (2006) Disassembly sequencing using genetic algorithm. Int J Adv Manuf Technol 30(5):497–506

    Article  Google Scholar 

  12. Kuo TC (2013) Waste electronics and electrical equipment disassembly and recycling using petri net analysis: considering the economic value and environmental impacts. Comput Ind Eng 65(1):54–64

    Article  Google Scholar 

  13. Lambert A (1999) Linear programming in disassembly/clustering sequence generation. Comput Ind Eng 36(4):723–738

    Article  Google Scholar 

  14. Li B, Li C, Cui X et al (2020) A disassembly sequence planning method with team-based genetic algorithm for equipment maintenance in hydropower station. IEEE Access 8:47538–47555

    Article  Google Scholar 

  15. Li JR, Khoo LP, Tor SB (2002) A novel representation scheme for disassembly sequence planning. Int J Adv Manuf Technol 20(8):621–630

    Article  Google Scholar 

  16. Lu C, Li X, Yu W et al (2021) Sensor network sensing coverage optimization with improved artificial bee colony algorithm using teaching strategy. Computing 103(7):1439–1460

    Article  MATH  Google Scholar 

  17. Makhadmeh SN, Abasi AK, Al-Betar MA (2022) Hybrid multi-verse optimizer with grey wolf optimizer for power scheduling problem in smart home using iot. J Supercomput 78(9):11794–11829

    Article  Google Scholar 

  18. Min SS, Zhu XJ, Zhu X (2010) Mechanical product disassembly and/or graph construction. In: 2010 International Conference on Measuring Technology and Mechatronics Automation

  19. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  20. Ren Y, Tian G, Zhao F et al (2017) Selective cooperative disassembly planning based on multi-objective discrete artificial bee colony algorithm. Eng Appl Artif Intell 64:415–431

    Article  Google Scholar 

  21. Sefati S, Mousavinasab M, Zareh Farkhady R (2022) Load balancing in cloud computing environment using the grey wolf optimization algorithm based on the reliability: performance evaluation. J Supercomput 78(1):18–42

    Article  Google Scholar 

  22. Sharma P, Gupta A, Aggarwal A et al (2020) The health of things for classification of protein structure using improved grey wolf optimization. J Supercomput 76(2):1226–1241

    Article  Google Scholar 

  23. Su SJ, Fang XF, Li F (2011) Research on the disassembly sequence planning for mechanical product. In: Applied mechanics and materials. Trans Tech Publ, pp. 1300–1304

  24. Tian G, Zhou MC, Li P (2017) Disassembly sequence planning considering fuzzy component quality and varying operational cost. IEEE Trans Autom Sci Eng 15(2):748–760

    Article  Google Scholar 

  25. Trivedi RR, Pawaskar DN, Shimpi RP (2021) Enhancement of travel range of electrostatically driven fixed-fixed microbeam in static and dynamic mode using mutated particle swarm optimization. Struct Multidiscip Optim 15–16:1–17

    Google Scholar 

  26. Tseng HE, Chang CC, Lee SC et al (2018) A block-based genetic algorithm for disassembly sequence planning. Expert Syst Appl 96:492–505

    Article  Google Scholar 

  27. Tseng HE, Chang CC, Lee SC et al (2019) Hybrid bidirectional ant colony optimization (hybrid baco): an algorithm for disassembly sequence planning. Eng Appl Artif Intell 83:45–56

    Article  Google Scholar 

  28. Tseng YJ, Yu FY, Huang FY (2011) A green assembly sequence planning model with a closed-loop assembly and disassembly sequence planning using a particle swarm optimization method. Int J Adv Manuf Technol 57(9–12):1183–1197

    Article  Google Scholar 

  29. Wu P, Wang H, Li B et al (2022) Disassembly sequence planning and application using simplified discrete gravitational search algorithm for equipment maintenance in hydropower station. Expert Syst Appl 208(118):046

    Google Scholar 

  30. Xia K, Gao L, Li W et al (2014) Disassembly sequence planning using a simplified teaching-learning-based optimization algorithm. Adv Eng Inf 28(4):518–527

    Article  Google Scholar 

  31. Xie J, Li X, Gao L (2021) Disassembly sequence planning based on a modified grey wolf optimizer. Int J Adv Manuf Technol 116(11):3731–3750

    Article  Google Scholar 

  32. Yeh WC (2011) Optimization of the disassembly sequencing problem on the basis of self-adaptive simplified swarm optimization. IEEE Trans Syst Man Cybern Part A Syst Humans 42(1):250–261

    Article  Google Scholar 

  33. Yeh WC (2012) Simplified swarm optimization in disassembly sequencing problems with learning effects. Comput Oper Res 39(9):2168–2177

    Article  MATH  Google Scholar 

  34. Zhang HC, Kuo TC (1996) A graph-based approach to disassembly model for end-of-life product recycling. In: Nineteenth IEEE/CPMT international electronics manufacturing technology symposium. IEEE, pp 247–254

  35. Zhang XF, Yu G, Hu ZY et al (2014) Parallel disassembly sequence planning for complex products based on fuzzy-rough sets. Int J Adv Manuf Technol 72(1–4):231–239

    Article  Google Scholar 

  36. Zhou M, DiCesare F (2012) Petri net synthesis for discrete event control of manufacturing systems, vol 204. Springer Science & Business Media, London

    MATH  Google Scholar 

Download references

Acknowledgements

This work described in this paper is supported by the National Natural Science Foundation of China (NSFC) (517419-07), the Open Fund of Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station (2017KJX06).

Author information

Author notes

  1. Xing Liu, Fanwu Chu, Bailin Li and Jiahao Gu have contributed equally to this work.

Authors and Affiliations

  1. College of Electrical Engineering and New Energy, Three Gorges University, Yichang, 443002, Hubei, China

    Wenlong Fu, Xing Liu, Bailin Li & Jiahao Gu

  2. Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, Three Gorges University, Yichang, 443002, Hubei, China

    Wenlong Fu & Bailin Li

  3. Quality Inspection and Test Center for Equipment of Electric Power, China Electric Power Research Institute, Wuhan, 430074, Hubei, China

    Fanwu Chu

Authors
  1. Wenlong Fu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xing Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Fanwu Chu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Bailin Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Jiahao Gu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Bailin Li.

Ethics declarations

Conflict of interest

No potential conflict of interest was reported by the author(s).

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix A: Tables 5, 6, 7 and 8 and Figs. 20, 21, 22, 23 and 24 mentioned in the article

Appendix A: Tables 5, 6, 7 and 8 and Figs. 20, 21, 22, 23 and 24 mentioned in the article

Table 5 The cost matrix of combination node 89 in flat plate water seal
Full size table
Table 6 The disassembly nodes information for flat plate water seal
Full size table
Table 7 The disassembly nodes information for bypass pipe
Full size table
Table 8 The disassembly information nodes for upstream pipe
Full size table
Fig. 20
figure 20

Assembly drawing of the flat plate water seal

Full size image
Fig. 21
figure 21

Assembly drawing of the bypass pipes and the upper pipes

Full size image
Fig. 22
figure 22

Explosion map of the flat plate water seal (cited from [15])

Full size image
Fig. 23
figure 23

Explosion map of the bypass pipe

Full size image
Fig. 24
figure 24

Explosion map of the upstream pipe

Full size image

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fu, W., Liu, X., Chu, F. et al. A disassembly sequence planning method with improved discrete grey wolf optimizer for equipment maintenance in hydropower station. J Supercomput 79, 4351–4382 (2023). https://doi.org/10.1007/s11227-022-04822-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04822-8

Keywords