On the end-of-life state oriented multi-objective disassembly line balancing problem

Abstract

The biggest difference between a disassembly line and an assembly line is that there are many uncertainties in structure and quality of the disassembled products in a disassembly line. The disassembly line balancing problem, considering the effect of end-of-life states caused by the uncertainty of the structure or the quality of the disassembled products, is addressed in this paper. A multi-objective mathematical model for the addressed problem is built with three optimization goals: minimizing the number of workstations, minimizing the idle index and minimizing the number of resources. Then a multi-objective hybrid migrating birds optimization algorithm is proposed, which uses a greedy random search operation based on embedding mechanism to generate neighborhood individuals. To avoid the problem of easily being trapped into a local optimum by a basic migrating birds optimization algorithm, a reset mechanism based on simulated annealing operation is set up to accept other solutions with a certain probability, so that the algorithm can escape out of a local optimum. By solving disassembly examples of different scales in the literature and comparing with the existing algorithms, the effectiveness and superiority of the proposed multi-objective hybrid migrating birds optimization algorithm is validated. Finally, the proposed model and algorithm are applied to solving two disassembly instances, and the solving results are compared with the single-objective optimal solution solved by LINGO 11.0 solver and the basic migrating birds optimization algorithm to further identify the performance of the proposed algorithm.

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

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

References

  1. Altekin, F. T. (2016). A piecewise linear model for stochastic disassembly line balancing. Ifac Papersonline,49(12), 932–937.

    Google Scholar 

  2. Altekin, F. T. (2017). A comparison of piecewise linear programming formulations for stochastic disassembly line balancing. International Journal of Production Research,55(24), 7412–7434.

    Google Scholar 

  3. Altekin, F. T., & Akkan, C. (2012). Task-failure-driven rebalancing of disassembly lines. International Journal of Production Research,50(18), 4955–4976.

    Google Scholar 

  4. Altekin, F. T., Kandiller, L., & Ozdemirel, N. E. (2008). Profit-oriented disassembly-line balancing. International Journal of Production Research,46(10), 2675–2693.

    Google Scholar 

  5. Avikal, S., Jain, R., & Mishra, P. K. (2014a). A Kano model, AHP and M-TOPSIS method-based technique for disassembly line balancing under fuzzy environment. Applied Soft Computing,25, 519–529.

    Google Scholar 

  6. Avikal, S., Mishra, P. K., & Jain, R. (2014b). A Fuzzy AHP and PROMETHEE method-based heuristic for disassembly line balancing problems. International Journal of Production Research,52(5), 1306–1317.

    Google Scholar 

  7. Aydemir-Karadag, A., & Turkbey, O. (2013). Multi-objective optimization of stochastic disassembly line balancing with station paralleling. Computers & Industrial Engineering,65(3), 413–425.

    Google Scholar 

  8. Bentaha, M. L., Battaia, O., & Dolgui, A. (2014a). Disassembly line balancing and sequencing under uncertainty. In T. K. Lien (Ed.), 21st Cirp conference on life cycle engineering (pp. 239–244). Amsterdam: Elsevier.

    Google Scholar 

  9. Bentaha, M. L., Battaia, O., & Dolgui, A. (2014b). Lagrangian relaxation for stochastic disassembly line balancing problem. In H. ElMaraghy (Ed.), Variety management in manufacturing: Proceedings of the 47th Cirp conference on manufacturing systems (pp. 56–60). Amsterdam: Elsevier.

  10. Bentaha, M. L., Battaia, O., & Dolgui, A. (2014c). A sample average approximation method for disassembly line balancing problem under uncertainty. Computers & Operations Research,51, 111–122.

    Google Scholar 

  11. Bentaha, M. L., Battaïa, O., & Dolgui, A. (2015a). An exact solution approach for disassembly line balancing problem under uncertainty of the task processing times. International Journal of Production Research,53(6), 1807–1818.

    Google Scholar 

  12. Bentaha, M. L., Battaia, O., Dolgui, A., & Hu, S. J. (2014d). Dealing with uncertainty in disassembly line design. CIRP Annals-Manufacturing Technology,63(1), 21–24.

    Google Scholar 

  13. Bentaha, M. L., Battaia, O., Dolgui, A., & Hu, S. J. (2015b). Second order conic approximation for disassembly line design with joint probabilistic constraints. European Journal of Operational Research,247(3), 957–967.

    Google Scholar 

  14. Bentaha, M. L., Dolgui, A., Battaia, O., Riggs, R. J., & Hu, J. (2018). Profit-oriented partial disassembly line design: Dealing with hazardous parts and task processing times uncertainty. International Journal of Production Research,56(24), 7220–7242.

    Google Scholar 

  15. Ding, L. P., Tan, J. R., Feng, Y. X., & Gao, Y. C. (2009). Multiobjective optimization for disassembly line balancing based on Pareto ant colony algorithm. Computer Integrated Manufacturing Systems,15(7), 1406–1413+1429.

    Google Scholar 

  16. Duman, E., Uysal, M., & Alkaya, A. F. (2012). Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem. Information Sciences,217, 65–77.

    Google Scholar 

  17. Fang, Y., Liu, Q., Li, M., Laili, Y., & Duc Truong, P. (2019a). Evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations. European Journal of Operational Research,276(1), 160–174.

    Google Scholar 

  18. Fang, Y., Ming, H., Li, M., Liu, Q., & Duc Truong, P. (2019b). Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time. International Journal of Production Research. https://doi.org/10.1080/00207543.2019.1602290.

    Article  Google Scholar 

  19. Gao, Y., Wang, Q., Feng, Y., Zheng, H., Zheng, B., & Tan, J. (2018). An energy-saving optimization method of dynamic scheduling for disassembly line. Energies,11(5), 1261–1278.

    Google Scholar 

  20. Gungor, A., & Gupta, S. M. (2001). A solution approach to the disassembly line balancing problem in the presence of task failures. International Journal of Production Research,39(7), 1427–1467.

    Google Scholar 

  21. Gungor, A., & Gupta, S. M. (2002). Disassembly line in product recovery. International Journal of Production Research,40(11), 2569–2589.

    Google Scholar 

  22. Gupta, S. M., Pochampally, K., & Kamarthi, S. V. (2001). Complications in disassembly line balancing. In S. M. Gupta (Ed.), Environmentally Conscious Manufacturing (pp. 289–298). Bellingham: SPIE.

    Google Scholar 

  23. Hezer, S., & Kara, Y. (2015). A network-based shortest route model for parallel disassembly line balancing problem. International Journal of Production Research,53(6), 1849–1865.

    Google Scholar 

  24. Hummel, D., & Beukenberg, M. (1989). Aerodynamic interference effects in formation flight of birds. Journal Für Ornithologie,130(1), 15–24.

    Google Scholar 

  25. Ilgin, M. A. (2019). A DEMATEL-based disassembly line balancing heuristic. Journal of Manufacturing Science and Engineering-Transactions of the Asme,141(2), 021002.

    Google Scholar 

  26. Ilgin, M. A., Akçay, H., & Araz, C. (2017). Disassembly line balancing using linear physical programming. International Journal of Production Research,55(20), 6108–6119.

    Google Scholar 

  27. Kalayci, C. B., & Gupta, S. M. (2013a). Artificial bee colony algorithm for solving sequence-dependent disassembly line balancing problem. Expert Systems with Applications,40(18), 7231–7241.

    Google Scholar 

  28. Kalayci, C. B., & Gupta, S. M. (2013b). A particle swarm optimization algorithm with neighborhood-based mutation for sequence-dependent disassembly line balancing problem. International Journal of Advanced Manufacturing Technology,69(1–4), 197–209.

    Google Scholar 

  29. Kalayci, C. B., & Gupta, S. M. (2014). A tabu search algorithm for balancing a sequence-dependent disassembly line. Production Planning & Control,25(2), 149–160.

    Google Scholar 

  30. Kalayci, C. B., Hancilar, A., Gungor, A., & Gupta, S. M. (2015a). Multi-objective fuzzy disassembly line balancing using a hybrid discrete artificial bee colony algorithm. Journal of Manufacturing Systems,37, 672–682.

    Google Scholar 

  31. Kalayci, C. B., Polat, O., & Gupta, S. M. (2015b). A variable neighbourhood search algorithm for disassembly lines. Journal of Manufacturing Technology Management,26(2), 182–194.

    Google Scholar 

  32. Kalayci, C. B., Polat, O., & Gupta, S. M. (2016). A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem. Annals of Operations Research,242(2), 321–354.

    Google Scholar 

  33. Kazancoglu, Y., & Ozturkoglu, Y. (2018). Integrated framework of disassembly line balancing with Green and business objectives using a mixed MCDM. Journal of Cleaner Production,191, 179–191.

    Google Scholar 

  34. Koc, A., Sabuncuoglu, I., & Erel, E. (2009). Two exact formulations for disassembly line balancing problems with task precedence diagram construction using an AND/OR graph. IIE Transactions,41(10), 866–881.

    Google Scholar 

  35. Li, Z., Cil, Z. A., Mete, S., & Kucukkoc, I. (2019). A fast branch, bound and remember algorithm for disassembly line balancing problem. International Journal of Production Research. https://doi.org/10.1080/00207543.2019.1630774.

    Article  Google Scholar 

  36. Li, L., Zhang, Z., Zhu, L., & Zou, B. (2018). Modeling and optimizing for multi-objective partial disassembly line balancing problem. Journal of Mechanical Engineering,54(3), 125–136.

    Google Scholar 

  37. Lissaman, P. B., & Shollenberger, C. A. (1970). Formation flight of birds. Science (New York, N.Y.),168(3934), 1003–1005.

    Google Scholar 

  38. Liu, J., & Wang, S. (2017). Balancing disassembly line in product recovery to promote the coordinated development of economy and environment. Sustainability,9(2), 1–15.

    Google Scholar 

  39. McGovern, S. M., & Gupta, S. M. (2004). 2-opt heuristic for the disassembly line balancing problem. In S. M. Gupta (Ed.), Environmentally conscious manufacturing Iii (pp. 71–84). Bellingham: SPIE.

    Google Scholar 

  40. McGovern, S. M., & Gupta, S. M. (2006). Ant colony optimization for disassembly sequencing with multiple objectives. International Journal of Advanced Manufacturing Technology,30(5–6), 481–496.

    Google Scholar 

  41. McGovern, S. M., & Gupta, S. M. (2007a). A balancing method and genetic algorithm for disassembly line balancing. European Journal of Operational Research,179(3), 692–708.

    Google Scholar 

  42. McGovern, S. M., & Gupta, S. M. (2007b). Combinatorial optimization analysis of the unary NP-complete disassembly line balancing problem. International Journal of Production Research,45(18–19), 4485–4511.

    Google Scholar 

  43. Meng, T., Pan, Q.-K., Li, J.-Q., & Sang, H.-Y. (2018a). An improved migrating birds optimization for an integrated lot-streaming flow shop scheduling problem. Swarm and Evolutionary Computation,38, 64–78.

    Google Scholar 

  44. Meng, K., Qian, X., Lou, P., & Zhang, J. (2018b). Smart recovery decision-making of used industrial equipment for sustainable manufacturing: Belt lifter case study. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1439-2.

    Article  Google Scholar 

  45. Mete, S., Cil, Z. A., Agpak, K., Ozceylan, E., & Dolgui, A. (2016a). A solution approach based on beam search algorithm for disassembly line balancing problem. Journal of Manufacturing Systems,41, 188–200.

    Google Scholar 

  46. Mete, S., Cil, Z. A., Ozceylan, E., & Agpak, K. (2016b). Resource constrained disassembly line balancing problem. Ifac Papersonline,49(12), 921–925.

    Google Scholar 

  47. Niroomand, S., Hadi-Vencheh, A., Sahin, R., & Vizvari, B. (2015). Modified migrating birds optimization algorithm for closed loop layout with exact distances in flexible manufacturing systems. Expert Systems with Applications,42(19), 6586–6597.

    Google Scholar 

  48. Özceylan, E., Kalayci, C. B., Güngör, A., & Gupta, S. M. (2018). Disassembly line balancing problem: A review of the state of the art and future directions. International Journal of Production Research,57(15–16), 4805–4827.

    Google Scholar 

  49. Paksoy, T., Gungor, A., Ozceylan, E., & Hancilar, A. (2013). Mixed model disassembly line balancing problem with fuzzy goals. International Journal of Production Research,51(20), 6082–6096.

    Google Scholar 

  50. Pistolesi, F., Lazzerini, B., Mura, M. D., & Dini, G. (2018). EMOGA: A hybrid genetic algorithm with extremal optimization core for multiobjective disassembly line balancing. IEEE Transactions on Industrial Informatics,14(3), 1089–1098.

    Google Scholar 

  51. Rao, R. V., Rai, D. P., & Balic, J. (2019). Multi-objective optimization of abrasive waterjet machining process using Jaya algorithm and PROMETHEE Method. Journal of Intelligent Manufacturing,30(5), 2101–2127.

    Google Scholar 

  52. Rayner, J. (1979). A new approach to animal flight mechanics. Journal of Experimental Biology,80(1), 17–54.

    Google Scholar 

  53. 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. International Journal of Production Research,55(24), 7302–7316.

    Google Scholar 

  54. Ren, Y., Zhang, C., Zhao, F., Tian, G., Lin, W., Meng, L., et al. (2018). Disassembly line balancing problem using interdependent weights-based multi-criteria decision making and 2-Optimal algorithm. Journal of Cleaner Production,174, 1475–1486.

    Google Scholar 

  55. Riggs, R. J., Battaïa, O., & Hu, S. J. (2015). Disassembly line balancing under high variety of end of life states using a joint precedence graph approach. Journal of Manufacturing Systems,37, 638–648.

    Google Scholar 

  56. Sankararao, B., & Chang, K. Y. (2011). Development of a robust multiobjective simulated annealing algorithm for solving multiobjective optimization problems. Industrial and Engineering Chemistry Research,50(50), 6728–6742.

    Google Scholar 

  57. Seidi, M., & Saghari, S. (2016). The balancing of disassembly line of automobile engine using genetic algorithm (GA) in fuzzy environment. Industrial Engineering and Management Systems,15(4), 364–373.

    Google Scholar 

  58. Sioud, A., & Gagne, C. (2018). Enhanced migrating birds optimization algorithm for the permutation flow shop problem with sequence dependent setup times. European Journal of Operational Research,264(1), 66–73.

    Google Scholar 

  59. Soto, R., Crawford, B., Almonacid, B., & Paredes, F. (2015). A migrating birds optimization algorithm for machine-part cell formation problems. In G. Sidorov & S. N. GaliciaHaro (Eds.), Advances in artificial intelligence and soft computing, Micai 2015, Pt I (pp. 270–281). Berlin: Springer.

    Google Scholar 

  60. Tiwari, M. K. (2008). A collaborative ant colony algorithm to stochastic mixed-model U-shaped disassembly line balancing and sequencing problem. International Journal of Production Research,46(6), 1405–1429.

    Google Scholar 

  61. Tongur, V., & Ulker, E. (2019). PSO-based improved multi-flocks migrating birds optimization (IMFMBO) algorithm for solution of discrete problems. Soft Computing,23(14), 5469–5484.

    Google Scholar 

  62. Tuncel, E., Zeid, A., & Kamarthi, S. (2014). Solving large scale disassembly line balancing problem with uncertainty using reinforcement learning. Journal of Intelligent Manufacturing,25(4), 647–659.

    Google Scholar 

  63. Wang, K., Li, X., & Gao, L. (2019a). A multi-objective discrete flower pollination algorithm for stochastic two-sided partial disassembly line balancing problem. Computers & Industrial Engineering,130, 634–649.

    Google Scholar 

  64. Wang, K., Li, X., Gao, L., & Garg, A. (2019b). Partial disassembly line balancing for energy consumption and profit under uncertainty. Robotics and Computer-Integrated Manufacturing,59, 235–251.

    Google Scholar 

  65. Wang, W., Mo, D. Y., Wang, Y., & Tseng, M. M. (2019c). Assessing the cost structure of component reuse in a product family for remanufacturing. Journal of Intelligent Manufacturing,30(2), 575–587.

    Google Scholar 

  66. Wang, K., Zhang, Z., Mao, L., & Li, L. (2017). Pareto artificial fish swarm algorithm for multi-objective disassembly line balancing problems. China Mechanical Engineering,28(2), 183–190.

    Google Scholar 

  67. Xia, X., Liu, W., Zhang, Z., Wang, L., Cao, J., & Liu, X. (2019). A balancing method of mixed-model disassembly line in random working environment. Sustainability,11(8), 2304.

    Google Scholar 

  68. Xiao, S., Wang, Y., Yu, H., & Nie, S. (2017). An entropy-based adaptive hybrid particle swarm optimization for disassembly line balancing problems. Entropy,19(11), 596.

    Google Scholar 

  69. Xie, Z., Jia, Y., Zhang, C., Shao, X., & Li, D. (2015). Blocking flow shop scheduling problem based on migrating birds optimization. Computer Integrated Manufacturing Systems,21(8), 2099–2107.

    Google Scholar 

  70. Yang, Y., Yuan, G., Zhuang, Q., & Tian, G. (2019). Multi-objective low-carbon disassembly line balancing for agricultural machinery using MDFOA and fuzzy AHP. Journal of Cleaner Production,233, 1465–1474.

    Google Scholar 

  71. Zhang, B., Pan, Q.-K., Gao, L., Zhang, X.-L., Sang, H.-Y., & Li, J.-Q. (2017a). An effective modified migrating birds optimization for hybrid flowshop scheduling problem with lot streaming. Applied Soft Computing,52, 14–27.

    Google Scholar 

  72. Zhang, Z., Tang, Q., Han, D., & Li, Z. (2019). Enhanced migrating birds optimization algorithm for U-shaped assembly line balancing problems with workers assignment. Neural Computing and Applications,31(11), 7501–7515.

    Google Scholar 

  73. Zhang, Z., Wang, K., Zhu, L., & Wang, Y. (2017b). A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem. Expert Systems with Applications,86, 165–176.

    Google Scholar 

  74. Zheng, F., He, J., Chu, F., & Liu, M. (2018). A new distribution-free model for disassembly line balancing problem with stochastic task processing times. International Journal of Production Research,56(24), 7341–7353.

    Google Scholar 

  75. Zhu, L., Zhang, Z., & Wang, Y. (2018). A Pareto firefly algorithm for multi-objective disassembly line balancing problems with hazard evaluation. International Journal of Production Research,56(24), 7354–7374.

    Google Scholar 

  76. Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach. IEEE Transactions on Evolutionary Computation,3(4), 257–271.

    Google Scholar 

Download references

Acknowledgements

This research was partially funded by the [National Natural Science Foundation of China] under Grant [Nos. 51205328, 51675450]; [Youth Foundation for Humanities and Social Sciences of Ministry of Education of China] under Grant [No. 18YJC630255]; and the [Sichuan Science and Technology Program] under Grant [No. 2019YFG0285].

Author information

Affiliations

Authors

Corresponding author

Correspondence to Zeqiang Zhang.

Ethics declarations

Conflict of interest

No potential conflict of interest was reported by the authors.

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

Verify currency and authenticity via CrossMark

Cite this article

Zhu, L., Zhang, Z., Wang, Y. et al. On the end-of-life state oriented multi-objective disassembly line balancing problem. J Intell Manuf 31, 1403–1428 (2020). https://doi.org/10.1007/s10845-019-01519-3

Download citation

Keywords

  • End-of-life states
  • Disassembly line balancing problem
  • Multi-objective optimization
  • Hybrid migrating birds optimization