Skip to main content

Advertisement

SpringerLink
Log in

End-of-life product disassembly with priority-based extraction of dangerous parts

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

The amount of electronic waste generated in the world is impressive. The USA alone yearly throw away 9.4 million tons of electronic devices: only 12.5% is recycled. One way to reduce this massive impact on the environment is to disassemble these devices with the aim of reusing and recycling as many parts as possible. Disassembling end-of-life products is a complex industrial process that may pose workers at risk because some parts of the product may contain dangerous materials. It is thus crucial to design efficient, sustainable and secure disassembly lines. This paper presents a multi-objective formulation of the Disassembly Line Balancing Problem (DLBP) which promotes efficiency and includes a new objective that increases the level of safety. The efficiency is guaranteed by balancing the idle times of the workstations, and by maximizing the profit and the level of feasibility of a disassembly sequence, which means disassembling the product as much as possible. Safety is maximized by extracting each dangerous part with a priority that is higher the more dangerous the part is. The most dangerous parts can thus be quickly removed from the product, thereby eliminating the exposure to the greatest risks. The disassembly continues with the execution of the tasks that remove the parts that are gradually less dangerous. Along with the DLBP formulation, this paper presents a genetic algorithm purposely designed to solve the problem. Two real-world case studies are discussed which entail the disassembly of a TV monitor and an air conditioner.

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.

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

Similar content being viewed by others

References

  • Al_Janabi, S., Alhashmi, S., & Adel, Z. (2019). Design (More-G) model based on renewable energy & knowledge constraint. In International conference on big data and networks technologies (pp. 271–295). Springer, Cham.

  • Alkaim, A. F., & Al_Janabi, S. (2019). Multi objectives optimization to gas flaring reduction from oil production. In International conference on big data and networks technologies (pp. 117–139). Springer, Cham.

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

    Article  Google Scholar 

  • BoussaïD, I., Lepagnot, J., & Siarry, P. (2013). A survey on optimization metaheuristics. Information Sciences, 237, 82–117.

    Article  Google Scholar 

  • Chern, C. C., Wang, H. M., & Huang, K. L. (2015). A heuristic master planning algorithm for recycling supply chain management. Journal of Intelligent Manufacturing, 28, 1–19.

    Google Scholar 

  • Coello Coello C. A., & Lechuga M. S. (2002). MOPSO: A proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 congress on evolutionary computation. CEC’02 (Vol. 2, pp. 1051–1056) Honolulu, USA.

  • Colledani, M., Copani, G., & Tolio, T. (2014). De-manufacturing systems. Procedia CIRP, 17, 14–19.

    Article  Google Scholar 

  • Colledani, M., & Tolio, T. (2013). Integrated process and system modelling for the design of material recycling systems. CIRP Annals-Manufacturing Technology, 62(1), 447–452.

    Article  Google Scholar 

  • Dalle Mura, M., & Dini, G. (2017). A multi-objective software tool for manual assembly line balancing using a genetic algorithm. CIRP Journal of Manufacturing Science and Technology, 19, 72–83.

    Article  Google Scholar 

  • Deb, K. (2014). Multi-objective optimization. In E. Burke, G. Kendal (Eds.), Search methodologies. Boston, MA: Springer. https://doi.org/10.1007/978-1-4614-6940-7_15.

  • 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 Transactions on Evolutionary Computation, 18(4), 577–601.

    Article  Google Scholar 

  • Ding, L. P., Feng, Y. X., Tan, J. R., & Gao, Y. C. (2010). A new multi-objective ant colony algorithm for solving the disassembly line balancing problem. The International Journal of Advanced Manufacturing Technology, 48(5–8), 761–771.

    Article  Google Scholar 

  • Dokeroglu, T., Sevinc, E., Kucukyilmaz, T., & Cosar, A. (2019). A survey on new generation metaheuristic algorithms. Computers & Industrial Engineering, 137, 106040.

    Article  Google Scholar 

  • Habibi, M. K. K., Battaïa, O., Cung, V. D., & Dolgui, A. (2014). Integrated procurement–disassembly problem. In IFIP international conference on advances in production management systems (pp. 482–490). Springer, Berlin.

  • Holland, J. H. (1975). Adaptation in natural and artificial systems. An introductory analysis with application to biology, control, and artificial intelligence. Ann Arbor: University of Michigan Press.

    Google Scholar 

  • Huang, C. C., Liang, W. Y., & Yi, S. R. (2015). Cloud-based design for disassembly to create environmentally friendly products. Journal of Intelligent Manufacturing, 28, 1–16.

    Article  Google Scholar 

  • Kalayci, C. B., & Gupta, S. M. (2013). 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Lazzerini, B., & Pistolesi, F. (2014). Classifying workers into risk sensibility profiles: A neural network approach. In: 2014 European modelling symposium, Pisa, pp. 33–38. https://doi.org/10.1109/EMS.2014.24.

  • Lazzerini, B., & Pistolesi, F. (2018a). Multiobjective personnel assignment exploiting workers’ sensitivity to risk. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(8), 1267–1282.

    Article  Google Scholar 

  • Lazzerini, B., & Pistolesi, F. (2018b). An integrated optimization system for safe job assignment based on human factors and behavior. IEEE Systems Journal, 12(2), 1158–1169.

    Article  Google Scholar 

  • Liu, J., Zhou, Z., Pham, D. T., Xu, W., Yan, J., Liu, A., et al. (2018). An improved multi-objective discrete bees algorithm for robotic disassembly line balancing problem in remanufacturing. The International Journal of Advanced Manufacturing Technology, 97, 1–26.

    Article  Google Scholar 

  • Marler, R. T., & Arora, J. S. (2004). Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, 26(6), 369–395.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • McGovern, S. M., & Gupta, S. M. (2006b). The disassembly line: Balancing and modeling. New York: McGraw-Hill. ISBN 9780071626057.

    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, 188–200.

    Article  Google Scholar 

  • 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, 1–23.

    Google Scholar 

  • Pistolesi, F., & Lazzerini, B. (2019). TeMA: A tensorial memetic algorithm for many-objective parallel disassembly sequence planning in product refurbishment. IEEE Transactions on Industrial Informatics, 15(6), 3743–3753.

    Article  Google Scholar 

  • Pistolesi, F., Lazzerini, B., Dalle, Mura M., & 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.

    Article  Google Scholar 

  • Rickli, J. L., & Camelio, J. A. (2013). Multi-objective partial disassembly optimization based on sequence feasibility. Journal of Manufacturing Systems, 32(1), 281–293.

    Article  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, 638–648.

    Article  Google Scholar 

  • Sivanandam, S. N., & Deepa, S. N. (2007). Introduction to genetic algorithms. Berlin: Springer. ISBN 9783540731894.

    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.

    Article  Google Scholar 

  • United Nations. Committee of Experts on the Transport of Dangerous Goods (2009). Recommendations on the transport of dangerous goods: Model regulations. Vol. 1, 16th revised version. United Nations Publications, ISBN 978-92-1-139136-7.

  • Vongbunyong, S., Kara, S., & Pagnucco, M. (2013). Application of cognitive robotics in disassembly of products. CIRP Annals-Manufacturing Technology, 62(1), 31–34.

    Article  Google Scholar 

  • Zhang, Q., & Li, H. (2007). MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712–731.

    Article  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, 165–176.

    Article  Google Scholar 

  • Zhang, X. F., Yu, G., Hu, Z. Y., Pei, C. H., & Ma, G. Q. (2014). Parallel disassembly sequence planning for complex products based on fuzzy-rough sets. The International Journal of Advanced Manufacturing Technology, 72(1–4), 231–239.

    Article  Google Scholar 

  • Zitzler, E., Laumanns, M., & Thiele, L. (2002). SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization, from evolutionary methods for design, Optimisation and Control. Barcelona: CIMNE.

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Italian Ministry of Education and Research (MIUR) in the framework of the CrossLab project (Departments of Excellence).

Author information

Authors and Affiliations

  1. Department of Civil and Industrial Engineering, University of Pisa, Largo Lazzarino, 1 – 56122, Pisa, Italy

    Michela Dalle Mura & Gino Dini

  2. Department of Information Engineering, University of Pisa, Largo Lazzarino, 1 – 56122, Pisa, Italy

    Francesco Pistolesi & Beatrice Lazzerini

Authors
  1. Michela Dalle Mura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francesco Pistolesi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gino Dini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Beatrice Lazzerini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francesco Pistolesi.

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

Dalle Mura, M., Pistolesi, F., Dini, G. et al. End-of-life product disassembly with priority-based extraction of dangerous parts. J Intell Manuf 32, 837–854 (2021). https://doi.org/10.1007/s10845-020-01592-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-020-01592-z

Keywords

Search

Navigation