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.
Similar content being viewed by others
References
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
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
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
Amen M (2000) An exact method for cost-oriented assembly line balancing. Int J Prod Econ 64(1):187–195
Rosenberg O, Ziegler H (1992) A comparison of heuristic algorithms for cost-oriented assembly line balancing. Z Oper Res 36(6):477–495
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
Hahn R (1972) Produktionsplanung bei Linienfertigung. de Gruyter,
Steffen R (1977) Produktionsplanung bei Fließbandfertigung. Gabler, Wiesbaden
Amen M (2000) Heuristic methods for cost-oriented assembly line balancing: a survey. Int J Prod Econ 68(1):1–14
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
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
Erel E, Sabuncuoglu I, Sekerci H (2005) Stochastic assembly line balancing using beam search. Int J Prod Res 43(7):1411–1426
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
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
Levitin G, Rubinovitz J, Shnits B (2006) A genetic algorithm for robotic assembly line balancing. Eur J Oper Res 168(3):811–825
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
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
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
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
Sivasankaran P, Shahabudeen P (2014) Literature review of assembly line balancing problems. Int J Adv Manuf Technol 73(9–12):1665–1694
Mukund Nilakantan J, Ponnambalam S (2015) Robotic U-shaped assembly line balancing using particle swarm optimization. Eng Optim 48(2):231–252
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
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
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
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
Nearchou AC (2008) Multi-objective balancing of assembly lines by population heuristics. Int J Prod Res 46(8):2275–2297
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
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
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
Davis L (1985) Applying adaptive algorithms to epistatic domains. In: IJCAI:162–164
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),
Feoktistov V (2006) Differential evolution. Springer
Mohamed AW, Sabry HZ, Khorshid M (2012) An alternative differential evolution algorithm for global optimization. J Adv Res 3(2):149–165
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-016-9086-2