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Discrete cuckoo search algorithms for two-sided robotic assembly line balancing problem

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Abstract

Robotics are extensively utilized in modern industry to replace human labor and achieve high automation and flexibility. In order to produce large-size products, two-sided assembly lines are widely applied, where robotics can be employed to operate tasks on workstations. Since the applied traditional optimization methods are limited, the current work presented a new discrete cuckoo search algorithm to solve the two-sided robotic assembly line balancing problem. The original cuckoo search algorithm was modified by employing neighbor operations. Furthermore, a new procedure to generate individuals to replace the abandoned nests was developed to enhance the intensification. Since the considered problem has two subproblems, namely the robot allocation and assembly line balancing, the present work extended the cuckoo search algorithm to cooperative coevolutionary paradigm by dividing the cuckoos into two sub-swarms, each addressing a subproblem. In order to emphasize the exploration, a restart mechanism was employed. The proposed discrete algorithm’s evolution process and convergence were compared with another two popular optimization algorithms, namely the genetic algorithm and particle swarm optimization algorithm. Computational study on the proposed algorithms and other five recent algorithms along with statistical analysis demonstrated that the proposed methods yielded promising results.

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References

  1. Boysen N, Fliedner M, Scholl A (2007) A classification of assembly line balancing problems. Eur J Oper Res 183(2):674–693. doi:10.1016/j.ejor.2006.10.010

    Article  MATH  Google Scholar 

  2. Levitin G, Rubinovitz J, Shnits B (2006) A genetic algorithm for robotic assembly line balancing. Eur J Oper Res 168(3):811–825. doi:10.1016/j.ejor.2004.07.030

    Article  MathSciNet  MATH  Google Scholar 

  3. Li Z, Tang Q, Zhang L (2016) Minimizing energy consumption and cycle time in two-sided robotic assembly line systems using restarted simulated annealing algorithm. J Clean Prod 135:508–522. doi:10.1016/j.jclepro.2016.06.131

    Article  Google Scholar 

  4. Li Z, Tang Q, Zhang L (2017) Two-sided assembly line balancing problem of type I: improvements, a simple algorithm and a comprehensive study. Comput Oper Res 79:78–93. doi:10.1016/j.cor.2016.10.006

    Article  MathSciNet  MATH  Google Scholar 

  5. Bartholdi JJ (1993) Balancing two-sided assembly lines: a case study. Int J Prod Res 31(10):2447–2461. doi:10.1080/00207549308956868

    Article  Google Scholar 

  6. Li Z, Janardhanan MN, Tang Q, Nielsen P (2016) Co-evolutionary particle swarm optimization algorithm for two-sided robotic assembly line balancing problem. Adv Mech Eng 8(9):14. doi:10.1177/1687814016667907

    Article  Google Scholar 

  7. Rubinovitz J, Bukchin J (1991) Design and balancing of robotic assembly lines. In: Proceedings of the Fourth World Conference on Robotics Research, Pittsburgh, PA

  8. Rubinovitz J, Bukchin J, Lenz E (1993) RALB—a heuristic algorithm for design and balancing of robotic assembly lines. CIRP Ann Manuf Technol 42(1):497–500. doi:10.1016/S0007-8506(07)62494-9

    Article  Google Scholar 

  9. 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. doi:10.1016/j.cie.2008.09.027

    Article  Google Scholar 

  10. 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. doi:10.1016/j.jmsy.2011.10.002

    Article  Google Scholar 

  11. Nilakantan JM, Ponnambalam SG, Jawahar N, Kanagaraj G (2015) Bio-inspired search algorithms to solve robotic assembly line balancing problems. Neural Comput Appl 26(6):1379–1393. doi:10.1007/s00521-014-1811-x

    Article  Google Scholar 

  12. Mukund Nilakantan J, Ponnambalam SG (2016) Robotic U-shaped assembly line balancing using particle swarm optimization. Eng Optim 48(2):231–252. doi:10.1080/0305215X.2014.998664

    Article  MathSciNet  Google Scholar 

  13. Kim YK, Kim Y, Kim YJ (2000) Two-sided assembly line balancing: a genetic algorithm approach. Prod Plan Control 11(1):44–53. doi:10.1080/095372800232478

    Article  Google Scholar 

  14. Lee TO, Kim Y, Kim YK (2001) Two-sided assembly line balancing to maximize work relatedness and slackness. Comput Ind Eng 40(3):273–292. doi:10.1016/S0360-8352(01)00029-8

    Article  Google Scholar 

  15. Wu E-F, Jin Y, Bao J-S, Hu X-F (2008) A branch-and-bound algorithm for two-sided assembly line balancing. Int J Adv Manuf Technol 39(9):1009–1015. doi:10.1007/s00170-007-1286-3

    Article  Google Scholar 

  16. Xiaofeng H, Erfei W, Jinsong B, Ye J (2010) A branch-and-bound algorithm to minimize the line length of a two-sided assembly line. Eur J Oper Res 206(3):703–707. doi:10.1016/j.ejor.2010.02.034

    Article  MATH  Google Scholar 

  17. Baykasoglu A, Dereli T (2008) Two-sided assembly line balancing using an ant-colony-based heuristic. Int J Adv Manuf Technol 36(5):582–588. doi:10.1007/s00170-006-0861-3

    Article  Google Scholar 

  18. Kucukkoc I, Zhang DZ (2016) Mixed-model parallel two-sided assembly line balancing problem: a flexible agent-based ant colony optimization approach. Comput Ind Eng 97:58–72. doi:10.1016/j.cie.2016.04.001

    Article  Google Scholar 

  19. Kucukkoc I, Zhang DZ (2015) Type-E parallel two-sided assembly line balancing problem: mathematical model and ant colony optimisation based approach with optimised parameters. Comput Ind Eng 84:56–69. doi:10.1016/j.cie.2014.12.037

    Article  Google Scholar 

  20. Özcan U, Toklu B (2008) A tabu search algorithm for two-sided assembly line balancing. Int J Adv Manuf Technol 43(7):822–829. doi:10.1007/s00170-008-1753-5

    Article  Google Scholar 

  21. Kim YK, Song WS, Kim JH (2009) A mathematical model and a genetic algorithm for two-sided assembly line balancing. Comput Oper Res 36(3):853–865. doi:10.1016/j.cor.2007.11.003

    Article  MATH  Google Scholar 

  22. Kucukkoc I, Zhang DZ (2015) A mathematical model and genetic algorithm-based approach for parallel two-sided assembly line balancing problem. Prod Plan Control 26(11):874–894. doi:10.1080/09537287.2014.994685

    Article  Google Scholar 

  23. Özcan U, Toklu B (2009) Balancing of mixed-model two-sided assembly lines. Comput Ind Eng 57(1):217–227. doi:10.1016/j.cie.2008.11.012

    Article  MATH  Google Scholar 

  24. Khorasanian D, Hejazi SR, Moslehi G (2013) Two-sided assembly line balancing considering the relationships between tasks. Comput Ind Eng 66(4):1096–1105. doi:10.1016/j.cie.2013.08.006

    Article  Google Scholar 

  25. Özbakır L, Tapkan P (2011) Bee colony intelligence in zone constrained two-sided assembly line balancing problem. Expert Syst Appl 38(9):11947–11957. doi:10.1016/j.eswa.2011.03.089

    Article  Google Scholar 

  26. Tang Q, Li Z, Zhang L (2016) An effective discrete artificial bee colony algorithm with idle time reduction techniques for two-sided assembly line balancing problem of type-II. Comput Ind Eng 97:146–156. doi:10.1016/j.cie.2016.05.004

    Article  Google Scholar 

  27. Li Z, Tang Q, Zhang L (2016) Minimizing the cycle time in two-sided assembly lines with assignment restrictions: improvements and a simple algorithm. Math Probl Eng 2016 (Article ID 4536426):1–15. doi:10.1155/2016/4536426

    MathSciNet  Google Scholar 

  28. Aghajani M, Ghodsi R, Javadi B (2014) Balancing of robotic mixed-model two-sided assembly line with robot setup times. Int J Adv Manuf Technol 74(5):1005–1016. doi:10.1007/s00170-014-5945-x

    Article  Google Scholar 

  29. Rajabioun R (2011) Cuckoo optimization algorithm. Appl Soft Comput 11(8):5508–5518. doi:10.1016/j.asoc.2011.05.008

    Article  Google Scholar 

  30. Yang XS, Suash D (2009) Cuckoo search via Lévy flights. In: Nature & biologically inspired computing, 2009. NaBIC 2009. World congress on, 9–11 Dec 2009, pp 210–214. doi:10.1109/NABIC.2009.5393690

  31. Marichelvam MK, Prabaharan T, Yang XS (2014) Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan. Appl Soft Comput 19:93–101. doi:10.1016/j.asoc.2014.02.005

    Article  Google Scholar 

  32. Ouaarab A, Ahiod B, Yang X-S (2014) Discrete cuckoo search algorithm for the travelling salesman problem. Neural Comput Appl 24(7):1659–1669. doi:10.1007/s00521-013-1402-2

    Article  Google Scholar 

  33. Saif U, Guan Z, Liu W, Wang B, Zhang C (2014) Multi-objective artificial bee colony algorithm for simultaneous sequencing and balancing of mixed model assembly line. Int J Adv Manuf Technol 75(9):1809–1827. doi:10.1007/s00170-014-6153-4

    Article  Google Scholar 

  34. Montgomery DC (2008) Design and analysis of experiments. Wiley, New York

    Google Scholar 

  35. Dey N, Samanta S, Yang XS, Das A, Chaudhuri SS (2013) Optimisation of scaling factors in electrocardiogram signal watermarking using cuckoo search. Int J Bio-Inspired Comput 5(5):315–326

    Article  Google Scholar 

  36. Ashour AS, Samanta S, Dey N, Kausar N, Abdessalemkaraa WB, Hassanien AE (2015) Computed tomography image enhancement using cuckoo search: a log transform based approach. J Signal Inf Process 6(3):244

    Google Scholar 

  37. Dey N, Samanta S, Chakraborty S, Das A, Chaudhuri SS, Suri JS (2014) Firefly algorithm for optimization of scaling factors during embedding of manifold medical information: an application in ophthalmology imaging. J Med Imaging Health Inform 4(3): 384–394

    Article  Google Scholar 

  38. Chatterjee S, Sarkar S, Hore S, Dey N, Ashour AS, Balas VE (2016) Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Comput Applic. doi:10.1007/s00521-016-2190-2

    Article  Google Scholar 

Download references

Acknowledgements

This research work is funded by the National Natural Science Foundation of China (Grant No. 51275366) (Qiuhua Tang).

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Correspondence to Amira S. Ashour.

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Li, Z., Dey, N., Ashour, A.S. et al. Discrete cuckoo search algorithms for two-sided robotic assembly line balancing problem. Neural Comput & Applic 30, 2685–2696 (2018). https://doi.org/10.1007/s00521-017-2855-5

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  • DOI: https://doi.org/10.1007/s00521-017-2855-5

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