Skip to main content

Advertisement

SpringerLink
Log in

A comparative analysis of meta-heuristic methods on disassembly line balancing problem with stochastic time

  • Original Research
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

The balancing of the disassembly line directly affects the productivity of the disassembly process. The disassembly line balancing (DLB) problem can be determined as assigning the tasks to serial workstations to optimize some performance measures like number of workstations, cycle time, removing hazardous parts earlier, etc. The aim of the paper is to develop an efficient heuristic algorithm to minimize the number of workstations under a pre-known cycle time. In this paper, a genetic algorithm (GA) and a constructive heuristic based on the Dijkstra algorithm is proposed to solve the DLB problem with stochastic task times that is caused by the nature of disassembly operation. The proposed algorithms are tested on benchmark problems and compared with the results of the piecewise-linear model (PLM) and simulated annealing (SA). The average relative percentage deviation is applied to transfer the obtained number of workstations. The results obtained by GA are clearly superior in all tests problem according to average relative percentage deviation. Moreover, the proposed constructive heuristic based on the Dijkstra algorithm is also superior to PLM and SA algorithm with respect to number of workstations and the computational times. The proposed approaches can be a very competitive and promising tool for further research in DLB literature and real cases in industries according to test results. Disassembly lines which need less time or number of workstations for balancing may be simply designed by the proposed techniques.

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

Similar content being viewed by others

References

  • Ağpak, K., & Gökçen, H. (2007). A chance-constrained approach to stochastic line balancing problem. European Journal of Operational Research, 180(3), 098–1115.

    Google Scholar 

  • Agrawal, S., & 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 

  • Altekin, F. T. (2016). A piecewise linear model for stochastic disassembly line balancing. IFAC-Papers on Line, 49(12), 932–937.

    Google Scholar 

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

    Google Scholar 

  • Altekin, F. T., Bayındır, Z. P., & Gümüşkaya, V. (2016). Remedial actions for disassembly lines with stochastic task times. Computers and Industrial Engineering, 99, 78–96.

    Google Scholar 

  • 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 

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

    Google Scholar 

  • Bagher, M., Zandieh, M., & Farsijani, H. (2011). Balancing of stochastic U-type assembly lines: An imperialist competitive algorithm. International Journal of Advanced Manufacturing Technology, 54, 271–285.

    Google Scholar 

  • Baykasoğlu, A., & Özbakır, L. (2007). Stochastic U-line balancing using genetic algorithms. International Journal of Advanced Manufacturing Technology, 32, 139–147.

    Google Scholar 

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

    Google Scholar 

  • Celik, E., Kara, Y., & Atasagun, Y. (2014). A new approach for rebalancing of U-lines with stochastic task times using ant colony optimization algorithm. International Journal of Production Research, 52(24), 7262–7275.

    Google Scholar 

  • Chiang, W. C., & Urban, T. L. (2006). The stochastic u-line balancing problem: A heuristic procedure. European Journal of Operational Research, 175(3), 1767–1781.

    Google Scholar 

  • Çil, Z. A., Kizilay, D., Li, Z., & Öztop, H. (2022). Two-sided disassembly line balancing problem with sequence-dependent setup time: A constraint programming model and artificial bee colony algorithm. Expert Systems with Applications., 203, 117529.

    Google Scholar 

  • Çil, Z. A., Mete, S., & Serin, F. (2020). Robotic disassembly line balancing problem: A mathematical model and ant colony optimization approach. Applied Mathematical Modelling, 86, 335–348.

    Google Scholar 

  • Deniz, N., & Ozcelik, F. (2019). An extended review on disassembly line balancing with bibliometric & social network and future study realization analysis. Journal of Cleaner Production, 225(1), 697–715.

    Google Scholar 

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

    Google Scholar 

  • Fang, Y., Ming, H., Li, M., Liu, Q., & Pham, D. T. (2020). Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time. International Journal of Production Research, 58(3), 846–862.

    Google Scholar 

  • Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley.

    Google Scholar 

  • Güngör, A., and Gupta, S.M., 1999, Disassembly line balancing, Proceedings of the 1999 Annual Meeting of the Northeast Decision Sciences Institute, 193–19

  • Güngör, 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), 142–1467.

    Google Scholar 

  • He, J., Chu, F., Zheng, F., & Liu, M. (2021). A green-oriented bi-objective disassembly line balancing problem with stochastic task processing times. Annals of Operations Research, 296, 71–93.

    Google Scholar 

  • 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 

  • 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 

  • Ilgin, M. A., & Gupta, S. M. (2010). Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art. Journal of Environmental Management, 91, 563–591.

    Google Scholar 

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

    Google Scholar 

  • Kalayci, C. B., & Gupta, S. M. (2013b). Ant colony optimization for sequence-dependent disassembly line balancing problem. Journal of Manufacturing Technology Management, 24(3), 413–427.

    Google Scholar 

  • Kalaycılar, E. G., Azizoğlu, M., & Yeralan, S. (2016). A disassembly line balancing problem with fixed number of workstations. European Journal of Operational Research, 249(2), 592–604.

    Google Scholar 

  • 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 

  • Ketzenberg, M. E., Souza, G. C., & Guide, V. D. R. (2003). Mixed assembly and disassembly operations for remanufacturing’. Production and Operations Management, 12(3), 320–335.

    Google Scholar 

  • Koç, A., Sabuncuoğlu, 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 

  • Li, Z., Çil, Z. A., Mete, S., & Kucukkoc, I. (2020). A fast branch, bound and remember algorithm for disassembly line balancing problem. International Journal of Production Research, 58(11), 3220–3234.

    Google Scholar 

  • Li, Z., Kucukkoc, I., & Zhang, Z. (2019). Iterated local search method and mathematical model for sequence-dependent U-shaped disassembly line balancing problem. Computers & Industrial Engineering. https://doi.org/10.1016/j.cie.2019.106056

    Article  Google Scholar 

  • Liu, M., Liu, X., Chu, F., Zheng, F., & Chu, C. 2020b. An exact method for disassembly line balancing problem with limited distributional information. International Journal of Production Research, 1–18.

  • Liu, M., Liu, X., Chu, F., Zheng, F., & Chu, C. (2020a). Robust disassembly line balancing with ambiguous task processing times. International Journal of Production Research, 58(19), 5806–5835.

    Google Scholar 

  • 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 

  • 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 

  • Mete, S., Çil, Z. A., Ağpak, K., Özceylan, E., & Dolgui, A. (2016). A solution approach based on beam search algorithm for disassembly line balancing problem. Journal of Manufacturing Systems, 41(1), 188–200.

    Google Scholar 

  • Mete, S., Çil, Z. A., Celik, E., & Ozceylan, E. (2019). Supply-driven rebalancing of disassembly lines: A novel mathematical model approach. Journal of Cleaner Production, 213, 1157–1164.

    Google Scholar 

  • Mete, S., Çil, Z. A., Özceylan, E., Ağpak, K., & Battaïa, O. (2018). An optimisation support for the design of hybrid production lines including assembly and disassembly tasks. International Journal of Production Research, 56(24), 7375–7389.

    Google Scholar 

  • Özceylan, E., Kalayci, C. B., Güngör, A., & Gupta, S. M. (2019). 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 

  • Pistolesi, F., Lazzerini, B., Dalle Mura, M., & Dini, G. (2017). EMOGA: A hybrid genetic algorithm with extremal optimization core for multi objective disassembly line balancing. IEEE Transactions on Industrial Informatics, 14(3), 1089–1098.

    Google Scholar 

  • 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 

  • 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. Journal of Cleaner Production, 174, 1475–1486.

    Google Scholar 

  • 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(3), 638–648.

    Google Scholar 

  • Roshani, A., & Giglio, D. (2017). Simulated annealing algorithms for the multi-manned assembly line balancing problem: Minimising cycle time. International Journal of Production Research, 55(10), 2731–2751.

    Google Scholar 

  • Serin, F., Mete, S., & Çelik, E. (2019). An efficient algorithm for U-type assembly line re-balancing problem with stochastic task times. Assembly Automation., 39(4), 581–595.

    Google Scholar 

  • Silverman, F. N., & Carter, J. C. (1986). A cost-based methodology for stochastic line balancing with intermittent line stoppages. Management Science, 32(4), 455–463.

    Google Scholar 

  • 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 

  • Urban, T. L., & Chiang, W. C. (2006). An optimal piecewise-linear program for the u-line balancing problem with stochastic task times. European Journal of Operational Research, 168, 771–782.

    Google Scholar 

  • Wang, K., Li, X., & Gao, L. (2019). Modelling and optimization of multi-objective partial disassembly line balancing problem considering hazard and profit. Journal of Cleaner Production, 211, 115–133.

    Google Scholar 

  • 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), 1–14.

    Google Scholar 

  • Yang, X. S. (2020). Nature-inspired optimization algorithms. Academic Press.

    Google Scholar 

  • Yin, T., Zhang, Z., & Jiang, J. (2021). A Pareto-discrete hummingbird algorithm for partial sequence-dependent disassembly line balancing problem considering tool requirements. Journal of Manufacturing Systems, 60, 406–428.

    Google Scholar 

  • Yin, T., Zhang, Z., Zhang, Y., Wu, T., & Liang, W. (2022). Mixed-integer programming model and hybrid driving algorithm for multi-product partial disassembly line balancing problem with multi-robot workstations. Robotics and Computer-Integrated Manufacturing, 73, 102251.

    Google Scholar 

  • Zhang, Y., Zhang, Z., Guan, C., & Xu, P. (2022). Improved whale optimisation algorithm for two-sided disassembly line balancing problems considering part characteristic indexes. International Journal of Production Research, 60(8), 2553–2571.

    Google Scholar 

  • 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 Systems with Applications, 86(1), 165–176.

    Google Scholar 

  • Zhu, X., Zhang, Z., & Hu, J. (2014). An ant colony optimization algorithm for multi-objective disassembly line balancing problem. China Mechanical Engineering, 25(8), 1075–1079.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Gaziantep University, Gaziantep, Turkey

    Süleyman Mete & Eren Özceylan

  2. Department of Computer Engineering, Mersin University, Mersin, Turkey

    Faruk Serin

  3. Department of Industrial Engineering, Izmir Democracy University, Izmir, Turkey

    Zeynel Abidin Çil

  4. Department of Transportation and Logistics, Istanbul University, Istanbul, Turkey

    Erkan Çelik

Authors
  1. Süleyman Mete
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Faruk Serin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zeynel Abidin Çil
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Erkan Çelik
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eren Özceylan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eren Özceylan.

Additional information

Publisher's Note

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

Appendices

Appendix 1

See Tables 6.

Table 6 The results of the stochastic DLB for small data set
Full size table

Appendix 2

See Table 7.

Table 7 The results of the stochastic DLB for medium data set
Full size table

Appendix 3

See Table 8.

Table 8 The results of the stochastic DLB for large data set
Full size table

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

Mete, S., Serin, F., Çil, Z.A. et al. A comparative analysis of meta-heuristic methods on disassembly line balancing problem with stochastic time. Ann Oper Res 321, 371–408 (2023). https://doi.org/10.1007/s10479-022-04910-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-022-04910-1

Keywords

Search

Navigation