Skip to main content

Advertisement

SpringerLink
Log in

A multi-objective algorithm for U-shaped disassembly line balancing with partial destructive mode

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The disassembly line is the best way to deal with large-scale waste electrical and electronic equipment. Balancing of disassembly line is a hot and challenging problem in recent years. Given the uncertainty factors including corrosion and deformation of parts and components of waste products, this paper introduces the destructive mode and uncertainty disassembly time into the disassembly line and establishes a multi-objective disassembly line balancing model, considering partial destructive mode and U-shaped layout. The model aims to reduce the number of stations, balance the workload and reduce energy consumption while increasing the disassembly profit. A new multi-objective discrete flower pollination algorithm is proposed to solve the problem. Both task assignment and disassembly modes are considered in the encoding and decoding strategies of the flowers. Combining the discrete characteristics of the problem, the cross-pollination and self-pollination behaviors of the algorithm are redefined. The performance of the proposed algorithm is verified by solving two classical examples and by comparing with seven meta-heuristic algorithms. Then the proposed model and method are applied to a television disassembly line of a disassembly enterprise in China. The disassembly schemes of the proposed algorithm are superior to that of the five classical multi-objective algorithms. The results show that the proposed method can improve the performance of the disassembly line.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Xia K, Gao L, Wang L, Li W, Li X, Ijomah W (2016) Service-oriented disassembly sequence planning for electrical and electronic equipment waste. Electron Commer Res Appl 20:59–68

    Google Scholar 

  2. Gungor A, Gupta SM (2002) Disassembly line in product recovery. Int J Prod Res 40(11):2569–2589

    MATH  Google Scholar 

  3. Gungor A, Gupta SM (2001) A solution approach to the disassembly line balancing problem in the presence of task failures. Int J Prod Res 39(7):1427–1467

    Google Scholar 

  4. McGovern SM, Gupta SM (2007) A balancing method and genetic algorithm for disassembly line balancing. Eur J Oper Res 179(3):692–708

    MATH  Google Scholar 

  5. Wang K, Li X, Gao L, Garg A (2019) Partial disassembly line balancing for energy consumption and profit under uncertainty. Robot Comput-Integr Manuf 59:235–251

    Google Scholar 

  6. Bentaha ML, Dolgui A, Battaia O, Riggs RJ, Hu J (2018) Profit-oriented partial disassembly line design: dealing with hazardous parts and task processing times uncertainty. Int J Prod Res 56(24):7220–7242

    Google Scholar 

  7. Deniz N, Ozcelik F (2019) An extended review on disassembly line balancing with bibliometric & social network and future study realization analysis. J Clean Prod 225:697–715

    Google Scholar 

  8. Song X, Zhou W, Pan X, Feng K (2014) Disassembly sequence planning for electro-mechanical products under a partial destructive mode. Assem Autom 34(1):106–114

    Google Scholar 

  9. Agrawal S, Tiwari MK (2008) A collaborative ant colony algorithm to stochastic mixed-model U-shaped disassembly line balancing and sequencing problem. Int J Prod Res 46(6):1405–1429

    MATH  Google Scholar 

  10. Wang K, Li X, Gao L (2019) A multi-objective discrete flower pollination algorithm for stochastic two-sided partial disassembly line balancing problem. Comput Ind Eng 130:634–649

    Google Scholar 

  11. Bentaha ML, Battaia O, Dolgui A (2015) An exact solution approach for disassembly line balancing problem under uncertainty of the task processing times. Int J Prod Res 53(6):1807–1818

    Google Scholar 

  12. Kalayci CB, Hancilar A, Gungor A, Gupta SM (2015) Multi-objective fuzzy disassembly line balancing using a hybrid discrete artificial bee colony algorithm. J Manuf Syst 37:672–682

    Google Scholar 

  13. Zhang Z, Wang K, Zhu L, Wang Y (2017) A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem. Expert Syst Appl 86:165–176

    Google Scholar 

  14. Ren Y, Zhang C, Zhao F, Tian G, Lin W, Meng L, Li H (2018) Disassembly line balancing problem using interdependent weights-based multi-criteria decision making and 2-optimal algorithm. J Clean Prod 174:1475–1486

    Google Scholar 

  15. McGovern SM, Gupta SM (2006) Ant colony optimization for disassembly sequencing with multiple objectives. Int J Adv Manuf Technol 30(5):481–496

    Google Scholar 

  16. Altekin FT, Kandiller L, Ozdemirel NE (2008) Profit-oriented disassembly-line balancing. Int J Prod Res 46(10):2675–2693

    MATH  Google Scholar 

  17. Ren Y, Yu D, Zhang C, Tian G, Meng L, Zhou X (2017) An improved gravitational search algorithm for profit-oriented partial disassembly line balancing problem. Int J Prod Res 55(24):7302–7316

    Google Scholar 

  18. Wang K, Li X, Gao L (2019) Modeling and optimization of multi-objective partial disassembly line balancing problem considering hazard and profit. J Clean Prod 211:115–133

    Google Scholar 

  19. McGovern SM, Gupta SM (2007) Combinatorial optimization analysis of the unary NP-complete disassembly line balancing problem. Int J Prod Res 45(18–19):4485–4511

    MATH  Google Scholar 

  20. Avikal S, Jain R, Mishra P (2014) A Kano model, AHP and M-TOPSIS method-based technique for disassembly line balancing under fuzzy environment. Appl Soft Comput 25:519–529

    Google Scholar 

  21. Zhang H, Cao X, Ho JKL, Chow TWS (2017) Object-level video advertising: an optimization framework. IEEE Trans Ind Inform 13(2):520–531

    Google Scholar 

  22. Zhang H, Llorca J, Davis CC, Milner SD (2012) Nature-inspired self-organization, control, and optimization in heterogeneous wireless networks. IEEE Trans Mob Comput 11(7):1207–1222

    Google Scholar 

  23. Yang X-S, Karamanoglu M, He X (2014) Flower pollination algorithm: A novel approach for multiobjective optimization. Eng Optim 46(9):1222–1237

    MathSciNet  Google Scholar 

  24. Fouad A, Gao X-Z (2019) A novel modified flower pollination algorithm for global optimization. Neural Comput Appl 31(8):3875–3908

    Google Scholar 

  25. Zhou Y, Wang R, Zhao C, Luo Q, Metwally MA (2019) Discrete greedy flower pollination algorithm for spherical traveling salesman problem. Neural Comput Appl 31(7):2155–2170

    Google Scholar 

  26. Abdel-Basset M, El-Shahat D, El-Henawy I (2019) Solving 0-1 knapsack problem by binary flower pollination algorithm. Neural Comput Appl 31(9):5477–5495

    Google Scholar 

  27. Mittal N, Singh U, Salgotra R, Bansal M (2019) An energy-efficient stable clustering approach using fuzzy-enhanced flower pollination algorithm for WSNs. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04251-4

    Article  Google Scholar 

  28. Kalayci CB, Gupta SM (2013) Artificial bee colony algorithm for solving sequence-dependent disassembly line balancing problem. Expert Syst Appl 40(18):7231–7241

    Google Scholar 

  29. Kalayci CB, Gupta SM (2013) A particle swarm optimization algorithm with neighborhood-based mutation for sequence-dependent disassembly line balancing problem. Int J Adv Manuf Technol 69(1–4):197–209

    Google Scholar 

  30. Kalayci CB, Gupta SM (2013) Ant colony optimization for sequence-dependent disassembly line balancing problem. J Manuf Technol Manag 24(3):413–427

    Google Scholar 

  31. Kalayci CB, Gupta SM (2014) A tabu search algorithm for balancing a sequence-dependent disassembly line. Prod Plan Control 25(2):149–160

    Google Scholar 

  32. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Google Scholar 

  33. Ding L, Feng Y, Tan J, Gao Y (2010) A new multi-objective ant colony algorithm for solving the disassembly line balancing problem. Int J Adv Manuf Technol 48(5):761–771

    Google Scholar 

  34. Kalayci CB, Gupta SM (2013) Solving sequence-dependent disassembly line balancing problem using a hybrid genetic algorithm. In: Proceedings for the Northeast Region Decision Sciences Institute, pp 1119–1128

  35. Kalayci CB, Gupta SM (2013) River formation dynamics approach for sequence-dependent disassembly line balancing problem. In: Gupta SM (ed) Reverse supply chains: issues and analysis. CRC Press, Boca Raton, FL, pp 289–312

    Google Scholar 

  36. Kalayci CB, Gupta SM (2013) Simulated annealing algorithm for solving sequence-dependent disassembly line balancing problem. IFAC Proc Vol 46(9):93–98

    Google Scholar 

  37. Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans Evol Comput 18(4):577–601

    Google Scholar 

  38. Li M, Yang S, Liu X (2014) Shift-based density estimation for Pareto-based algorithms in many-objective optimization. IEEE Trans Evol Comput 18(3):348–365

    Google Scholar 

  39. Wang R, Purshouse RC, Fleming PJ (2013) Preference-inspired coevolutionary algorithms for many-objective optimization. IEEE Trans Evol Comput 17(4):474–494

    Google Scholar 

  40. Yang S, Li M, Liu X, Zheng J (2013) A grid-based evolutionary algorithm for many-objective optimization. IEEE Trans Evol Comput 17(5):721–736

    Google Scholar 

  41. Bader J, Zitzler E (2011) HypE: An algorithm for fast hypervolume-based many-objective optimization. Evol Comput 19(1):45–76

    Google Scholar 

  42. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput 3(4):257–271

    Google Scholar 

  43. Zitzler E, Thiele L, Laumanns M, Fonseca CM, Fonseca VGd (2003) Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans Evol Comput 7(2):117–132

    Google Scholar 

Download references

Acknowledgements

This study was funded by the National Natural Science Foundation of China (Grant No. 51721092), the Natural Science Foundation of Hubei Province (Grant No. 2018CFA078), and Program for HUST Academic Frontier Youth Team (Grant No. 2017QYTD04).

Author information

Authors and Affiliations

  1. State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Kaipu Wang, Liang Gao & Xinyu Li

Authors
  1. Kaipu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinyu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xinyu Li.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, K., Gao, L. & Li, X. A multi-objective algorithm for U-shaped disassembly line balancing with partial destructive mode. Neural Comput & Applic 32, 12715–12736 (2020). https://doi.org/10.1007/s00521-020-04721-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04721-0

Keywords

Search

Navigation