Skip to main content

Advertisement

Log in

Differential evolution algorithm for solving RALB problem using cost- and time-based models

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Assembly process is one of the important aspects in manufacturing industries. Industries are extensively using advanced technologies in assembly lines recently such as robots instead of human labor. Cost associated with human labor such as wages, training, safety, and employee management are eliminated with the help of robots. Investments on assembly lines are cost intensive, and industries continuously need to maximize their utilization. In this paper, a cost-based robotic assembly line balancing (RALB) problem with an objective of minimizing assembly line cost and cycle time is addressed. Moreover, there is no research reported on concurrently optimizing cycle time and assembly line cost for a robotic assembly line system to date. The objective of this paper is to propose models with dual focus on time and cost to minimize the cycle time and total assembly line cost simultaneously. Time-based model with the primary focus to optimize cycle time and the cost-based model with the primary focus to optimize total assembly line cost are developed. Due to NP-hard nature, differential evolution (DE) is the algorithm used to solve the RALB problem. Straight and U-shaped robotic assembly line problems are solved using the proposed algorithm, and the detailed comparisons of the results obtained are presented. While comparing straight and U-shaped RALB problems, assembly line cost and cycle time obtained by U-shaped RALB problems are better than the straight RALB problems. The proposed models have significant managerial implications, and these have been discussed in detail.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Zhong RY, Dai Q, Qu T, Hu G, Huang GQ (2013) RFID-enabled real-time manufacturing execution system for mass-customization production. Robot Comput Integr Manuf 29(2):283–292

    Article  Google Scholar 

  2. Padrón M, de los A. Irizarry M, Resto P, HP M (2009) A methodology for cost-oriented assembly line balancing problems. J Manuf Technol Manag 20(8):1147–1165

    Article  Google Scholar 

  3. Chica M, Bautista J, Cordón Ó, Damas S (2016) A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand. Omega 58:55–68

    Article  Google Scholar 

  4. Amen M (2000) An exact method for cost-oriented assembly line balancing. Int J Prod Econ 64(1):187–195

    Article  Google Scholar 

  5. Rosenberg O, Ziegler H (1992) A comparison of heuristic algorithms for cost-oriented assembly line balancing. Z Oper Res 36(6):477–495

    MATH  Google Scholar 

  6. Hazir O, Delorme X, Dolgui A A Survey on cost and profit oriented assembly line balancing. In: 19th World Congress of The International Federation of Automatic Control, Cape Town, South Africa, 2014. vol 1. pp 6159–6167

  7. Hahn R (1972) Produktionsplanung bei Linienfertigung. de Gruyter,

    Book  MATH  Google Scholar 

  8. Steffen R (1977) Produktionsplanung bei Fließbandfertigung. Gabler, Wiesbaden

    Book  Google Scholar 

  9. Amen M (2000) Heuristic methods for cost-oriented assembly line balancing: a survey. Int J Prod Econ 68(1):1–14

    Article  Google Scholar 

  10. Amen M (2001) Heuristic methods for cost-oriented assembly line balancing: a comparison on solution quality and computing time. Int J Prod Econ 69(3):255–264

    Article  Google Scholar 

  11. Scholl A, Becker C (2005) A note on “An exact method for cost-oriented assembly line balancing”. Int J Prod Econ 97(3):343–352

    Article  Google Scholar 

  12. Erel E, Sabuncuoglu I, Sekerci H (2005) Stochastic assembly line balancing using beam search. Int J Prod Res 43(7):1411–1426

    Article  MATH  Google Scholar 

  13. Roshani A, Fattahi P, Roshani A, Salehi M, Roshani A (2012) Cost-oriented two-sided assembly line balancing problem: a simulated annealing approach. Int J Comput Integr Manuf 25(8):689–715

    Article  Google Scholar 

  14. Hazır Ö, Delorme X, Dolgui A (2015) A review of cost and profit oriented line design and balancing problems and solution approaches. Annual Reviews in Control

    Google Scholar 

  15. Levitin G, Rubinovitz J, Shnits B (2006) A genetic algorithm for robotic assembly line balancing. Eur J Oper Res 168(3):811–825

    Article  MathSciNet  MATH  Google Scholar 

  16. Gao J, Sun L, Wang L, Gen M (2009) An efficient approach for type II robotic assembly line balancing problems. Comput Ind Eng 56(3):1065–1080

    Article  Google Scholar 

  17. Nilakantan JM, Ponnambalam S, Jawahar N, Kanagaraj G (2015) Bio-inspired search algorithms to solve robotic assembly line balancing problems. Neural Comput & Applic 26(6):1379–1393

    Article  Google Scholar 

  18. Yoosefelahi A, Aminnayeri M, Mosadegh H, Ardakani HD (2012) Type II robotic assembly line balancing problem: an evolution strategies algorithm for a multi-objective model. J Manuf Syst 31(2):139–151

    Article  Google Scholar 

  19. Nilakantan JM, Huang GQ, Ponnambalam S (2015) An investigation on minimizing cycle time and total energy consumption in robotic assembly line systems. J Clean Prod 90:311–325

    Article  Google Scholar 

  20. Sivasankaran P, Shahabudeen P (2014) Literature review of assembly line balancing problems. Int J Adv Manuf Technol 73(9–12):1665–1694

    Article  Google Scholar 

  21. Mukund Nilakantan J, Ponnambalam S (2015) Robotic U-shaped assembly line balancing using particle swarm optimization. Eng Optim 48(2):231–252

    Article  MathSciNet  Google Scholar 

  22. Scholl A, Becker C (2006) State-of-the-art exact and heuristic solution procedures for simple assembly line balancing. Eur J Oper Res 168(3):666–693

    Article  MathSciNet  MATH  Google Scholar 

  23. Rashid MFF, Hutabarat W, Tiwari A (2012) A review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches. Int J Adv Manuf Technol 59(1–4):335–349

    Article  Google Scholar 

  24. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  25. Wang G-G, Hossein Gandomi A, Yang X-S, Hossein Alavi A (2014) A novel improved accelerated particle swarm optimization algorithm for global numerical optimization. Eng Comput 31(7):1198–1220

    Article  Google Scholar 

  26. Nearchou AC (2008) Multi-objective balancing of assembly lines by population heuristics. Int J Prod Res 46(8):2275–2297

    Article  MATH  Google Scholar 

  27. Karaboga N, Cetinkaya B (2004) Performance comparison of genetic and differential evolution algorithms for digital FIR filter design. Advances in information systems. Springer, In, pp. 482–488

    Google Scholar 

  28. Ponnambalam S, Aravindan P, Naidu GM (2000) A multi-objective genetic algorithm for solving assembly line balancing problem. Int J Adv Manuf Technol 16(5):341–352

    Article  Google Scholar 

  29. Das S, Abraham A, Chakraborty UK, Konar A (2009) Differential evolution using a neighborhood-based mutation operator. Evolutionary Computation, IEEE Transactions on 13(3):526–553

    Article  Google Scholar 

  30. Davis L (1985) Applying adaptive algorithms to epistatic domains. In: IJCAI:162–164

  31. Scholl A (ed) (1995) Data of assembly line balancing problems. Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL),

  32. Feoktistov V (2006) Differential evolution. Springer

  33. Mohamed AW, Sabry HZ, Khorshid M (2012) An alternative differential evolution algorithm for global optimization. J Adv Res 3(2):149–165

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J. Mukund Nilakantan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nilakantan, J.M., Nielsen, I., Ponnambalam, S.G. et al. Differential evolution algorithm for solving RALB problem using cost- and time-based models. Int J Adv Manuf Technol 89, 311–332 (2017). https://doi.org/10.1007/s00170-016-9086-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-016-9086-2

Keywords

Navigation