Journal of Heuristics

, Volume 20, Issue 3, pp 235–259 | Cite as

Solving a robotic assembly line balancing problem using efficient hybrid methods

  • Slim Daoud
  • Hicham Chehade
  • Farouk Yalaoui
  • Lionel Amodeo


In this paper we are studying a robotic assembly line balancing problem. The goal is to maximize the efficiency of the line and to balance the different tasks between the robots by defining the suitable tasks and components to assign to each robot. We are interested in a robotic line which consists of seizing the products on a moving conveyor and placing them on different location points. The performances evaluations of the system are done using a discret event simulation model. This latter has been developed with C++ language. As in our industrial application we are bounded by the execution time, we propose some resolution methods which define the suitable component and point positions in order to define the strategy of pick and place for each robot. These methods are based on the ant colony optimization, particle swarm optimization and genetic algorithms. To enhance the quality of the developed algorithms and to avoid local optima, we have coupled these algorithms with guided local search. After that, an exact method based on full enumeration is also developed to assess the quality of the developed methods. Then, we try to select the best algorithm which is able to get the best solutions with a small execution time. This is the main advantage of our methods compared to exact methods. This fact represents a great interest taking in consideration that the selected methods are used to manage the functioning of real industrial robotic assembly lines. Numerical results show that the selected algorithm performs optimally for the tested instances in a reasonable computation time and satisfies the industrial constraint.


Robotic assembly line balancing Metaheuristics Guided local search 



This research was supported by ARIES Packaging (France). The authors are also very grateful to the national association of technical research (ANRT) in France.


  1. Akpinar, S., Bayhan, G.M.: A hybrid genetic algorithm for mixed model assembly line balancing problem with parallel workstations and zoning constraints. Eng. Appl. Artif. Intell. 24, 449–457 (2011)CrossRefGoogle Scholar
  2. Bozma, H.I., Kalalioglu, M.E.: Multirobotcoordination in pick-and-placetasks on a movingconveyor. Robot. Comput. Integr. Manuf. 28, 530–538 (2012)CrossRefGoogle Scholar
  3. Bukchin, Y., Rabinowitch, I.: A branch-and bound based solution approach for the mixed-model assembly line-balancing problem for minimizing stations and taskduplication costs. Eur. J. Oper. Res. 174, 492–508 (2006)CrossRefMATHGoogle Scholar
  4. Chehade, H., Yalaoui, F., Amodeo, L., De Guglielmo, P.: Ant colony optimization for assembly lines design problem. Proceedings of the 8th international FLINS’08 conference on computational intelligence in decision and control, Madrid, Espagne, pp. 1135–1140 (2008)Google Scholar
  5. Chehade, H., Amodeo, L., Yalaoui, F.: A new efficient hybrid method for selecting machines and sizing buffers in assembly lines. J. Oper. Logist. 3, Z.I–Z.20 (2009)Google Scholar
  6. Chehade, H., Yalaoui, F., Amodeo, L., De Guglielmo, L.P.: Optimisation multiobjectif par colonies de fourmis pour le problme de dimensionnement de buffers. J. Decis. Syst. 18(2), 257–287 (2009)CrossRefGoogle Scholar
  7. Chen, J.C., Chen, C.C., Su, L.H., Wu, H.-B., Sun, C.-J.: Assembly line balancing in garment industry. Expert Syst. Appl. 39, 10073–10081 (2012)CrossRefGoogle Scholar
  8. Chu, P.C., Beasley, J.E.: A genetic algorithm for the multidimensional knapsack problem. J. Heuristics 4, 63–86 (1998)CrossRefMATHGoogle Scholar
  9. Chutima, P., Chimklai, P.: Multi-objective two-sided mixed-model assembly line balancing using particle swarm optimisation with negative knowledge. Comput. Ind. Eng. 62, 39–55 (2012)CrossRefGoogle Scholar
  10. Daoud, S., Yalaoui, F., Amodeo, L., Chehade, H., and Girard, T.: A particle swarm optimization algorithm for pick and place robotic systems. In: Proceedings of the 24th EURO conference (2010)Google Scholar
  11. Dorigo, M.: Optimization Learning and Natural Algorithms, PHD thesis, Politecnice Di Milano, (1992)Google Scholar
  12. Dongyun, W., Ping, Z., Luowei, L., Wang, K.: A Novel Particle Swarm Optimization Algorithm, Software Engineering and Service Sciences (ICSESS), 408–411 (2010)Google Scholar
  13. Dou, J., Li, J., Lv, Q.: A hybrid particle swarm algorithm for assembly line balancing problem of type 1.In: International conference on mechatronics and automation (ICMA), pp 1664–1669 (2011)Google Scholar
  14. Faisae Rashid, M.F., Hutabarat, W., Tiwari, A.: A review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches. Int. J. Adv. Manuf. Technol. 59, 335–349 (2011)CrossRefGoogle Scholar
  15. Gao, J., Linyan, S., Wang, L., Mitsuo, G.: An efficient approach for type II robotic assembly line balancing problems. Comput. Ind. Eng. 56, 1065–1080 (2009)CrossRefGoogle Scholar
  16. Hani, Y., Amodeo, L., Yalaoui, F., Chen, H.: Ant colony optimization for solving an industrial layout problem. Eur. J. Oper. Res. 183, 633–642 (2007)CrossRefMATHGoogle Scholar
  17. Ho, W., Ji, P.: An integratedschedulingproblem of PCB components on sequential pick-and-place machines: mathematical models and heuristic solutions. Expert Syst. Appl. 36, 7002–7010 (2009)CrossRefGoogle Scholar
  18. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  19. Huang, T., Wang, P.F., Mei, J.P., Zhao, X.M., Chetwynd, D.G.: Time minimum trajectory planning of a 2-DOF translational parallel robot for pick-and-place operations. CIRP Ann. Manuf. Technol. 56, 365–368 (2007)CrossRefGoogle Scholar
  20. Jian-sha, L., Ling-ling, J., and Xiu-lin, L.: Hybrid particle swarm optimization algorithm for assembly line balancing problem-2. In: 16th International Conference on Industrial Engineering and Engineering Management, IE&EM’09, pp. 979–983 (2009)Google Scholar
  21. Kelaiaiaa, R., Company, O., Zaatri, A.: Multiobjective optimization of a linear Delta parallel robot. Mech. Mach. Theory 50, 159–178 (2012)CrossRefGoogle Scholar
  22. Kennedy, J., and Eberhart, R.C.: Particle swarm optimization. In Proceeding of IEEE international conference on neural network, Perth, Australia, 4, 1942–1948 (1995)Google Scholar
  23. Khouja, M., Booth, D.E., Suh, M., Mahaney Jr, J.K.: Statistical procedures for task assignment and robot selection in assembly cells. Int. J. Comput. Integr. Manuf. 13, 95–106 (2000)CrossRefGoogle Scholar
  24. Kim, H., Park, S.: A strong cutting plane algorithm for the robotic assembly line balancing problem. Int. J. Prod. Res. 33, 2311–2323 (1995)CrossRefMATHGoogle Scholar
  25. Lee, J.-K., Lee, T.-E.: Automata-based supervisory control logic design for a multi-robot assembly cell. Int. J. Comput. Integr. Manuf. 15, 319–334 (2002)CrossRefGoogle Scholar
  26. Levitin, G., Rubinovitz, J., Schnits, B.: A genetic algorithm for robotic assembly line balancing. Eur. J. Oper. Res. 168, 811–825 (2006)CrossRefMATHGoogle Scholar
  27. Liang, Y.-C., Lee, Z.-H., Chen, Y.-S.: A novel ant colony optimization approach for on-line scheduling and due date determination. J. Heuristics 18(2), 571–591 (2012)Google Scholar
  28. May, F.B., Kaye, A.R., Mahmoud, S.A.: Control and communications for multiple, cooperating robots. Robot. Comput. Integr. Manuf. 6, 37–53 (1989)CrossRefGoogle Scholar
  29. Nicosia, G., Pacciarelli, D., Pacifici, A.: Optimally balancing assembly lines with different workstations. Discret. Appl. Mathe. 118, 99–113 (2002)CrossRefMATHMathSciNetGoogle Scholar
  30. Onut, S., Tuzkaya, U.R., Dogac, B.: A particle swarm optimization algorithm for the multiple-level warehouse layout design problem. Comput. Ind. Eng. Arch. 54(4), 783–799 (2008)CrossRefGoogle Scholar
  31. Qiu-gao, S.: Mixed-model assembly line balancing based on PSO-SA alternate algorithm. Intell. Comput. Technol. Autom. 2, 687–690 (2010)Google Scholar
  32. Rubinovitz, J., Bukchin, J.: Design and balancing of robotic assembly lines. In: Proceedings of the fourth world conference on on robotics research, Pittsburgh, PA, (1991)Google Scholar
  33. Sharma, S., Sharma, B.B., Dash, P., Choudhury, B.B.: Generation of optimized robotic assembly sequence using ant colony optimization. In: Proceeding of automation science and engineering IEEE- CASE, 894–899 (2008)Google Scholar
  34. Simaria, A.S., Vilarinho, P.M.: 2-ANTBAL: an ant colony optimisation algorithm for balancing two-sided assembly lines. Comput. Ind. Eng. 56, 489–506 (2009)CrossRefGoogle Scholar
  35. Suren, R.: Application and comparison of metaheuristic ant colony optimization and particle swarm optimization algorithms in manufacturing automation. In: Proceeding of 4th international conference on computer modelling and simulation, (2012)Google Scholar
  36. Tsai, D.M., Yao, M.J.: A line-balanced-base capacity planning procedure for series-type robotic assembly line. Int. J. Prod. Res. 31, 1901–1920 (1993)CrossRefGoogle Scholar
  37. Taylan Das, M., Canan Dlger, L.: Mathematical modelling, simulation and experimental verification of a scara robot. Simul. Model. Pract. Theory 13, 257–271 (2005)Google Scholar
  38. Tseng, H.-E., Wang, W.-P., Shih, H.-Y.: Using memetic algorithms with guided local search to solve assembly sequence planning. Expert Syst. Appl. 33, 451–467 (2007)CrossRefGoogle Scholar
  39. Voudouris, V., Tsang, E.: Partial constraint satisfaction problems and guided local search. Technical report, 337–356 (1996)Google Scholar
  40. Yalaoui, N., Mahdi, H., Amodeo, L., Yalaoui, F.: A new approach for workshop design. J. Intell. Manuf. 22, 933–951 (2009)CrossRefGoogle Scholar
  41. Yagmahan, B.: Mixed-model assembly line balancing using a multi-objective ant colony optimization approach. Expert Syst. Appl. 38, 12453–12461 (2011)CrossRefGoogle Scholar
  42. Yoosefelahi, A., Aminnayeri, M., Mosadegh, H., Ardakani, H.D.: Type II robotic assembly line balancing problem: an evolution strategies algorithm for a multi-objective model. J. Manuf. Syst. 31, 139–151 (2012)CrossRefGoogle Scholar
  43. Ze-qiang, Z., Chen, W., Lian-sheng, T., Bin, Z.: Ant algorithm with summation rules for assembly line balancing problem. In: International conference on management science and engineering. ICMSE, 369–374 (2007)Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Slim Daoud
    • 1
    • 2
  • Hicham Chehade
    • 1
    • 2
  • Farouk Yalaoui
    • 1
    • 2
  • Lionel Amodeo
    • 1
    • 2
  1. 1.ARIES PackagingTechnopole de l’Aube en ChampagneRosiresFrance
  2. 2.ICD, LOSITroyes University of TechnologyTroyesFrance

Personalised recommendations