The ASALB Problem with Processing Alternatives Involving Different Tasks: Definition, Formalization and Resolution

  • Liliana Capacho
  • Rafael Pastor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3982)


The Alternative Subgraphs Assembly Line Balancing Problem (ASALBP) considers assembly alternatives that determine task processing times and/or precedence relations among the tasks. Capacho and Pastor [3] formalized this problem and developed a mathematical programming model (MILP) in which the assembly alternatives are determined by combining all available processing alternatives of each existing sub-assembly. In this paper an extended definition of the ASALBP is presented in which assembly sub-processes involving different tasks are also considered. Additionally, a mathematical programming model is proposed to formalize and solve the extended version of the ASALBP, which also improves the performance of the former MILP model. Some computational results are included.


Precedence Relation Total Processing Time Assembly Line Balance Mathematical Programming Model Assembly Line Balance Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baybars, I.: A survey of exact algorithms for the simple assembly line balancing problem. Management Science 32, 909–932 (1986)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Becker, C., Scholl, A.: A survey on problems and methods in generalized assembly line balancing. Eur. J. of Op. Res. 168, 694–715 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Capacho, L., Pastor, R.: ASALBP: the Alternative Subgraphs Assembly Line Balancing Problem. Technical Report IOC-DT-P-2005-5. UPC. Spain (Jan. 2005)Google Scholar
  4. 4.
    Das, S., Nagendra, P.: Selection of routes in a flexible manufacturing facility. Int. Journal of Production Economics 48, 237–247 (1997)CrossRefGoogle Scholar
  5. 5.
    Gen, M., Tsujimura, Y., Li, Y.: Fuzzy assembly line balancing using genetic algorithms. Comp. & Ind. Eng. 31, 631–634 (1996)CrossRefGoogle Scholar
  6. 6.
    Kim, Y.K., Kim, Y.J., Kim, Y.: Genetic algorithms for assembly line balancing with various objectives. Comp. and Ind. Eng. 30(3), 397–409 (1996)CrossRefGoogle Scholar
  7. 7.
    Lapierre, S.D., Ruiz, A.B.: Balancing assembly lines: an industrial case study. J. of the Operational Research Society 55, 559–597 (2004)Google Scholar
  8. 8.
    Miltenburg, J.: Balancing and scheduling mixed-model U-shaped production lines. International Journal of Flexible Manufacturing Systems 14, 119–151 (2002)CrossRefGoogle Scholar
  9. 9.
    Pastor, R., Andres, C., Duran, A., Perez, M.: Tabu search algorithms for an industrial multi-product, multi-objective assembly line balancing problem, with reduction of task dispersion. J. Op. Res. Society 53, 1317–1323 (2002)zbMATHCrossRefGoogle Scholar
  10. 10.
    Scholl, A., Becker, C.: State-of-the-art exact and heuristic solution procedures for simple assembly line balancing. European J. of Op. Res. 168, 666–693 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Scholl, A., Klein, R.: SALOME: A bidirectional branch and bound procedure for assembly line balancing. INFORMS Journal on Comp. 9, 319–334 (1997)zbMATHCrossRefGoogle Scholar
  12. 12.
    Suresh, G., Sahu, S.: Stochastic assembly line balancing using simulated annealing. Int. Journal of Production Research 32, 1801–1810 (1994)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Liliana Capacho
    • 1
    • 2
  • Rafael Pastor
    • 2
  1. 1.Dpto. de I.O. – EISULA y CESIMOUniversidad de Los AndesMéridaVenezuela
  2. 2.IOC Research InstituteTechnical University of CataloniaBarcelonaSpain

Personalised recommendations