Soft Computing

, Volume 19, Issue 10, pp 2891–2903 | Cite as

Interactive preferences in multiobjective ant colony optimisation for assembly line balancing

  • Manuel Chica
  • Óscar Cordón
  • Sergio Damas
  • Joaquín Bautista
Methodologies and Application


In this contribution, we propose an interactive multicriteria optimisation framework for the time and space assembly line balancing problem. The framework allows decision maker interaction by means of reference points to obtain the most interesting non-dominated solutions. The principal components of the framework are the \(g\)-dominance preference scheme and a state-of-the-art memetic multiobjective ant colony optimisation approach. In addition, the framework includes a novel adaptive multi-colony mechanism to be able to handle the preferences in an interactive way. Results show how the multiobjective framework can interactively obtain the most useful solutions with higher convergence than previous a priori methods. The experimentation also makes use of original data of the Nissan Pathfinder engine and practical bounds to define industrially feasible solutions in a set of scenarios. By solving the problem in these scenarios, we show the search guidance advantages of using an interactive multiobjective ant colony optimisation method.


Interactive preferences Multiobjective ant colony optimisation Assembly line balancing problem 


  1. Barán B, Schaerer M (2003) A multiobjective ant colony system for vehicle routing problem with time windows. In: 21st IASTED international conference, Innsbruck, pp 97–102Google Scholar
  2. Battaïa O, Dolgui A (2013) A taxonomy of line balancing problems and their solution approaches. Int J Prod Econ 142(2):259–277CrossRefGoogle Scholar
  3. Bautista J, Pereira J (2007) Ant algorithms for a time and space constrained assembly line balancing problem. Eur J Oper Res 177(3):2016–2032CrossRefzbMATHGoogle Scholar
  4. Baybars I (1986) A survey of exact algorithms for the simple assembly line balancing problem. Manag Sci 32(8):909–932CrossRefzbMATHMathSciNetGoogle Scholar
  5. Said Ben L, Bechikh S, Ghédira K (2010) The r-dominance: a new dominance relation for interactive evolutionary multicriteria decision making. IEEE Trans Evol Comput 14(5):801–818Google Scholar
  6. Boysen N, Fliedner M, Scholl A (2007) A classification of assembly line balancing problems. Eur J Oper Res 183(2):674–693CrossRefzbMATHMathSciNetGoogle Scholar
  7. Branke J, Deb K, Miettinen K, Slowinski R (eds) (2008) Multiobjective optimization, interactive and evolutionary approaches. Lecture notes in computer science, vol 5252. Springer, BerlinGoogle Scholar
  8. Chica M, Cordón O, Damas S (2011) An advanced multi-objective genetic algorithm design for the time and space assembly line balancing problem. Comput Ind Eng 61(1):103–117CrossRefGoogle Scholar
  9. Chica M, Cordón O, Damas S, Bautista J (2010) Multiobjective, constructive heuristics for the 1/3 variant of the time and space assembly line balancing problem: ACO and random greedy search. Inf Sci 180:3465–3487CrossRefGoogle Scholar
  10. Chica M, Cordón O, Damas S, Bautista J (2010) A multiobjective GRASP for the 1/3 variant of the time and space assembly line balancing problem. Trends Appl Intell Syst Lect Notes Artif Intell 6098:656–665Google Scholar
  11. Chica M, Cordón O, Damas S, Bautista J (2011) Including different kinds of preferences in a multi-objective ant algorithm for time and space assembly line balancing on different nissan scenarios. Exp Syst Appl 38:709–720CrossRefGoogle Scholar
  12. Chica M, Cordón O, Damas S, Bautista J (2012) Multiobjective memetic algorithms for time and space assembly line balancing. Eng Appl Artif Intell 25(2):254–273CrossRefGoogle Scholar
  13. Chica M, Cordón O, Damas S, Bautista J (2013) A robustness information and visualization model for time and space assembly line balancing under uncertain demand. Int J Prod Econ 145:761–772CrossRefGoogle Scholar
  14. Coello CA, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems, 2nd edn. Springer, BerlinGoogle Scholar
  15. Deb K (1999) Solving goal programming problems using multi-objective genetic algorithms. In: IEEE congress on evolutionary computation (CEC 99). Washington (USA), pp 77–84Google Scholar
  16. Deb K, Sinha A, Korhonen PJ, Wallenius J (2010) An interactive evolutionary multiobjective optimization method based on progressively approximated value functions. IEEE Trans Evol Comput 14(5):723–739 Google Scholar
  17. Dorigo M, Gambardella L (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66CrossRefGoogle Scholar
  18. Figueira JR, Liefooghe A, Talbi EG, Wierzbicki AP (2010) A parallel multiple reference point approach for multi-objective optimization. Eur J Oper Res 205(2):390–400CrossRefzbMATHMathSciNetGoogle Scholar
  19. Fonseca CM, Fleming PJ (1996) On the performance assessment and comparison of stochastic multiobjective optimizers. In: Proceedings of the 4th international conference on parallel problem solving from nature (PPSN). Lecture notes in computer science, vol 1141. Berlin, Germany, pp 584–593Google Scholar
  20. Gandibleux X, Freville A (2000) Tabu search based procedure for solving the 0–1 multiobjective knapsack problem: the two objectives case. J Heurist 6(3):361–383CrossRefzbMATHGoogle Scholar
  21. Greiner D, Galván B, Emperador JM, Méndez M, Winter G (2011) Introducing reference point using g-dominance in optimum design considering uncertainties: an application in structural engineering. In: Evolutionary multi-criteria optimization. Lecture notes in computer science, vol 6576. Springer, Berlin, pp 389–403Google Scholar
  22. Iredi S, Merkle D, Middendorf M (2001) Bi-criterion optimization with multi colony ant algorithms. In: First intl. conference on evolutionary multi-criterion optimization (EMO’01). Springer, Berlin, pp 359–372Google Scholar
  23. Jaszkiewicz A (2002) Genetic local search for multiple objective combinatorial optimization. Eur J Oper Res 137(1):50–71CrossRefzbMATHMathSciNetGoogle Scholar
  24. Kamalian R, Takagi H, Agonino AM (2004) Optimized design of MEMS by evolutionary multi-objective optimization with interactive evolutionary computation. Springer, Berlin, pp 1030–1041Google Scholar
  25. Molina J, Santana LV, Hernández-Díaz AG, Coello CA, Caballero R (2009) g-dominance: reference point based dominance for multiobjective metaheuristics. Eur J Oper Res 197(2):17–24CrossRefzbMATHGoogle Scholar
  26. Phelps S, Koksalan M (2003) An interactive evolutionary metaheuristic for multiobjective combinatorial optimization. Manag Sci 49(12):1726–1738CrossRefzbMATHGoogle Scholar
  27. Rada-Vilela J, Chica M, Cordón O, Damas S (2013) A comparative study of multi-objective ant colony optimization algorithms for the time and space assembly line balancing problem. Appl Soft Comput 13(11):4370–4382CrossRefGoogle Scholar
  28. Rodríguez B, Molina J, Pérez F, Caballero R (2012) Interactive design of personalised tourism routes. Tour Manag 33(4):926–940CrossRefGoogle Scholar
  29. 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):666–693CrossRefzbMATHMathSciNetGoogle Scholar
  30. Scholl A, Klein C (1999) Balancing assembly lines effectively—a computational comparison. Eur J Oper Res 114(1):50–58CrossRefzbMATHGoogle Scholar
  31. Zitzler E, Thiele L, Laumanns M, Fonseca CM, da Fonseca VG (2003) Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans Evol Comput 7(2):117–132CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Manuel Chica
    • 1
  • Óscar Cordón
    • 1
    • 2
  • Sergio Damas
    • 1
  • Joaquín Bautista
    • 3
  1. 1.European Centre for Soft ComputingMieresSpain
  2. 2.DECSAI and CITIC-UGR, University of GranadaGranadaSpain
  3. 3.Universitat Politècnica de CatalunyaBarcelonaSpain

Personalised recommendations