Annals of Operations Research

, Volume 242, Issue 2, pp 321–354 | Cite as

A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem

Article

Abstract

For remanufacturing or recycling companies, a reverse supply chain is of prime importance since it facilitates in recovering parts and materials from end-of-life products. In reverse supply chains, selective separation of desired parts and materials from returned products is achieved by means of disassembly which is a process of systematic separation of an assembly into its components, subassemblies or other groupings. Due to its high productivity and suitability for automation, disassembly line is the most efficient layout for product recovery operations. A disassembly line must be balanced to optimize the use of resources (viz., labor, money and time). In this paper, we consider a sequence-dependent disassembly line balancing problem (SDDLBP) with multiple objectives that requires the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and optimizing the effectiveness of several measures considering sequence dependent time increments. A hybrid algorithm that combines a genetic algorithm with a variable neighborhood search method (VNSGA) is proposed to solve the SDDLBP. The performance of VNSGA was thoroughly investigated using numerous data instances that have been gathered and adapted from the disassembly and the assembly line balancing literature. Using the data instances, the performance of VNSGA was compared with the best known metaheuristic methods reported in the literature. The tests demonstrated the superiority of the proposed method among all the methods considered.

Keywords

Reverse supply chain Disassembly Assembly Sequence-dependent disassembly line balancing Metaheuristics Hybrid genetic algorithm Variable neighborhood search 

References

  1. 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.CrossRefGoogle Scholar
  2. Akpınar, S., & Bayhan, G. M. (2011). A hybrid genetic algorithm for mixed model assembly line balancing problem with parallel workstations and zoning constraints. Engineering Applications of Artificial Intelligence, 24(3), 449–457.CrossRefGoogle Scholar
  3. Altekin, F. T., & Akkan, C. (2012). Task-failure-driven rebalancing of disassembly lines. International Journal of Production Research, 50(18), 4955–4976.CrossRefGoogle 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.CrossRefGoogle Scholar
  5. Battaïa, O., & Dolgui, A. (2013). A taxonomy of line balancing problems and their solutionapproaches. International Journal of Production Economics, 142(2), 259–277.CrossRefGoogle Scholar
  6. Baybars, İ. (1986). A survey of exact algorithms for the simple assembly line balancing problem. Management Science, 32(8), 909–932.CrossRefGoogle Scholar
  7. Baykasoglu, A. (2006). Multi-rule multi-objective simulated annealing algorithm for straight and U type assembly line balancing problems. Journal of Intelligent Manufacturing, 17(2), 217–232.CrossRefGoogle Scholar
  8. Chiang, W.-C. (1998). The application of a tabu search metaheuristic to the assembly line balancing problem. Annals of Operations Research, 77, 209–227.CrossRefGoogle Scholar
  9. 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.CrossRefGoogle Scholar
  10. Gungor, A., & Gupta, S. M. (1999). Issues in environmentally conscious manufacturing and product recovery: A survey. Computers and Industrial Engineering, 36(4), 811–853.CrossRefGoogle Scholar
  11. Gungor, A., & Gupta, S. M. (2002). Disassembly line in product recovery. International Journal of Production Research, 40(11), 2569–2589.CrossRefGoogle Scholar
  12. Gupta, S. M., Erbis, E., & McGovern, S. M. (2004). Disassembly sequencing problem: A case study of a cell phone. In S. M. Gupta (Ed.), Environmentally Conscious Manufacturing IV. In Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE) (Vol. 5583, pp. 43–52). Bellingham: SPIE-International Society for Optical Engineering.Google Scholar
  13. Gurevsky, E., Battaïa, O., & Dolgui, A. (2012). Balancing of simple assembly lines under variations of task processing times. Annals of Operations Research, 201(1), 265–286.CrossRefGoogle Scholar
  14. Hansen, P., Mladenović, N., & Pérez, J. M. (2010). Developments of variable neighborhood search. Annals of Operations Research, 175(1), 367–407.CrossRefGoogle Scholar
  15. 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(3), 563–591.CrossRefGoogle Scholar
  16. Ilgin, M. A., & Gupta, S. M. (2012). Remanufacturing modeling and analysis. Boca Raton: CRC Press.CrossRefGoogle Scholar
  17. Kalayci, C. B., & Gupta, S. M. (2011). A hybrid genetic algorithm approach for disassembly line balancing. In Proceedings of the 42nd Annual Meeting of Decision Science Institute (DSI 2011), Boston (Vol. 1, pp. 2142–2148).Google Scholar
  18. Kalayci, C. B., & Gupta, S. M. (2012). A particle swarm optimization algorithm for solving disassembly line balancing problem. In Proceedings for the Northeast Region Decision Sciences Institute (NEDSI) (pp. 347–357). Northeast Decision Sciences Institute.Google Scholar
  19. Kalayci, C. B., & Gupta, S. M. (2013a). Ant colony optimization for sequence-dependent disassembly line balancing problem. Journal of Manufacturing Technology Management, 24(3), 413–427.CrossRefGoogle Scholar
  20. Kalayci, C. B., & Gupta, S. M. (2013b). Artificial bee colony algorithm for solving sequence-dependent disassembly line balancing problem. Expert Systems with Applications, 40(18), 7231–7241.CrossRefGoogle Scholar
  21. Kalayci, C. B., & Gupta, S. M. (2013c). 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.CrossRefGoogle Scholar
  22. Kalayci, C. B., & Gupta, S. M. (2003d). Simulated annealing algorithm for solving sequence-dependent disassembly line balancing problem. In IFAC Conference on Manufacturing Modelling, Management, and Control, Saint Petersburg, Russia (pp. 93–98).Google Scholar
  23. Kalayci, C. B., & Gupta, S. M. (2013e). A tabu search algorithm for balancing a sequence-dependent disassembly line. Production Planning and Control, 25(2), 149–160.CrossRefGoogle Scholar
  24. Kalayci, C. B., Gupta, S. M., & Nakashima, K. (2011). Bees Colony Intelligence in Solving Disassembly Line Balancing Problem. In Proceedings of the 2011 Asian Conference of Management Science and Applications (ACMSA2011), Sanya (pp. 34–41).Google Scholar
  25. Kalayci, C. B., Gupta, S. M., & Nakashima, K. (2012). A simulated annealing algorithm for balancing a disassembly line. In M. Matsumoto, Y. Umeda, K. Masui, & S. Fukushige (Eds.), Design for innovative value towards a sustainable society (pp. 714–719). New York: Springer.CrossRefGoogle Scholar
  26. Kilincci, O. (2011). Firing sequences backward algorithm for simple assembly line balancing problem of type 1. Computers and Industrial Engineering, 60(4), 830–839.CrossRefGoogle Scholar
  27. 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.CrossRefGoogle Scholar
  28. Kongar, E., & Gupta, S. M. (2005). Disassembly sequencing using genetic algorithm. The International Journal of Advanced Manufacturing Technology, 30(5–6), 497–506.Google Scholar
  29. Lapierre, S. D., Ruiz, A., & Soriano, P. (2006). Balancing assembly lines with tabu search. European Journal of Operational Research, 168(3), 826–837.CrossRefGoogle Scholar
  30. McGovern, S. M., & Gupta, S. M. (2005a). Ant colony optimization for disassembly sequencing with multiple objectives. The International Journal of Advanced Manufacturing Technology, 30(5–6), 481–496.Google Scholar
  31. McGovern, S. M., & Gupta, S. M. (2005). Uninformed and probabilistic distributed agent combinatorial searches for the unary NP-complete disassembly line balancing problem. In S. M. Gupta (Ed.), Environmentally conscious manufacturing V (1st ed., Vol. 5997, pp. 81–92). Boston: SPIE.Google Scholar
  32. McGovern, S. M., & Gupta, S. M. (2006). Ant colony optimization for disassembly sequencing with multiple objectives. The International Journal of Advanced Manufacturing Technology, 30(5), 481–496.CrossRefGoogle Scholar
  33. McGovern, S. M., & Gupta, S. M. (2007). A balancing method and genetic algorithm for disassembly line balancing. European Journal of Operational Research, 179(3), 692–708.CrossRefGoogle Scholar
  34. McGovern, S. M., & Gupta, S. M. (2011). The disassembly line: Balancing and modeling. New York: McGraw Hill.Google Scholar
  35. Mladenović, N., & Hansen, P. (1997). Variable neighborhood search. Computers and Operations Research, 24(11), 1097–1100.CrossRefGoogle Scholar
  36. Sabuncuoglu, I., Erel, E., & Tanyer, M. (2000). Assembly line balancing using genetic algorithms. Journal of Intelligent Manufacturing, 11(3), 295–310.CrossRefGoogle Scholar
  37. Scholl, A., Boysen, N., & Fliedner, M. (2008). The sequence-dependent assembly line balancing problem. OR Spectrum, 30(3), 579–609.CrossRefGoogle Scholar
  38. Scholl, A., & Klein, R. (1999). Balancing assembly lines effectively: A computational comparison. European Journal of Operational Research, 114(1), 50–58.CrossRefGoogle Scholar
  39. Sprecher, A. (1999). A competitive branch-and-bound algorithm for the simple assembly line balancing problem. International Journal of Production Research, 37(8), 1787–1816.CrossRefGoogle Scholar
  40. Tonge, F. M. (1961). A heuristic program of assembly line balancing. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
  41. Tseng, H. E., Chen, M. H., Chang, C. C., & Wang, W. P. (2008). Hybrid evolutionary multi-objective algorithms for integrating assembly sequence planning and assembly line balancing. International Journal of Production Research, 46(21), 5951–5977.Google Scholar
  42. Tuncel, E., Zeid, A., & Kamarthi, S. (2012). Solving large scale disassembly line balancing problem with uncertainty using reinforcement learning. Journal of Intelligent Manufacturing, 2, 1–13.Google Scholar
  43. Wang, H.-F., & Gupta, S. M. (2011). Green supply chain management: Product life cycle approach. New York: McGraw Hill.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Can B. Kalayci
    • 1
  • Olcay Polat
    • 1
  • Surendra M. Gupta
    • 2
  1. 1.Department of Industrial EngineeringPamukkale UniversityDenizli Turkey
  2. 2.Department of Mechanical and Industrial EngineeringNortheastern UniversityBostonUSA

Personalised recommendations