Progress in Artificial Intelligence

, Volume 5, Issue 1, pp 27–33 | Cite as

GRASP for sequencing mixed models in an assembly line with work overload, useless time and production regularity

  • Joaquín BautistaEmail author
  • Rocío Alfaro-Pozo
  • Cristina Batalla-García
Regular Paper


A GRASP algorithm is presented for solving a sequencing problem in a mixed-model assembly line. The problem is focused on obtaining a manufacturing sequence that completes the greatest possible amount of required work and fulfils the production regularity property. The implemented GRASP algorithm is compared with other resolution procedures by means of instances from a case study linked to the Nissan’s engine plant in Barcelona.


GRASP Sequencing Mixed-model assembly line Production mix preservation 



This work was funded by the Ministerio de Economía y Competitividad (Spanish Government) through the FHI-SELM2 (TIN2014-57497-P) project.


  1. 1.
    Battaïa, O., Dolgui, A.: A taxonomy of line balancing problems and their solution approaches. Int. J. Prod. Econ. 142(2), 259–277 (2013)CrossRefGoogle Scholar
  2. 2.
    Boysen, N., Fliedner, M., Scholl, A.: Sequencing mixed-model assembly lines: survey, classification and model critique. Eur. J. Oper. Res. 192(2), 349–373 (2009)zbMATHMathSciNetCrossRefGoogle Scholar
  3. 3.
    Bautista, J., Cano, A.: Solving mixed model sequencing problem in assembly lines with serial workstations with work overload minimization and interruption rules. Eur. J. Oper. Res. 210, 495–513 (2011)zbMATHCrossRefGoogle Scholar
  4. 4.
    Yano, C.A., Rachamadugu, R.: Sequencing to minimize work overload in assembly lines with product options. Manag. Sci. 37(5), 572–586 (1991)CrossRefGoogle Scholar
  5. 5.
    Bautista, J., Pereira, J., Adenso-Díaz, B.: A GRASP approach for the extended car sequencing problema. J. Sched. 11(1), 3–16 (2008)zbMATHMathSciNetCrossRefGoogle Scholar
  6. 6.
    Bautista, J., Cano, A., Alfaro, R., Batalla, C.: Impact of the production mix preservation on the ORV problem. In: Bielza, C., et al. (eds.) CAEPIA 2013, LNAI 8109, pp. 250–259. Springer, Berlin (2013)Google Scholar
  7. 7.
    Scholl, A., Klein, R., Domschke, W.: Pattern based vocabulary building for effectively sequencing mixed-model assembly lines. J. Heuristics 4(4), 359–381 (1998)zbMATHCrossRefGoogle Scholar
  8. 8.
    Omar, M., Sarker, R., Othman, W.A.M.: A just-in-time three-level integrated manufacturing system for linearly time-varying demand process. Appl. Math. Model. 37(3), 1275–1281 (2013)CrossRefGoogle Scholar
  9. 9.
    Fullerton, R.R., Kennedy, F.A., Widener, S.K.: Lean manufacturing and firm performance: the incremental contribution of lean management accounting practices. J. Oper. Manag. 32(7–8), 414–428 (2014)CrossRefGoogle Scholar
  10. 10.
    Bautista, J., Cano, A., Alfaro, R.: Modeling and solving a variant of the mixed-model sequencing problem with work overload minimisation and regularity constraints. An application in Nissan’s Barcelona Plant. Expert Syst. Appl. 39, 11001–11010 (2012)CrossRefGoogle Scholar
  11. 11.
    Feo, T.A., Resende, M.G.C.: Greedy randomized adaptive search procedures. J. Global Optim. 6(2), 109–133 (1995)zbMATHMathSciNetCrossRefGoogle Scholar
  12. 12.
    Resende, M.G.C., Ribeiro, C.C.: Greedy randomized adaptive search procedures: advances, hybridizations, and applications: In: Gendreau, M., Potvin, J.Y (eds.) Handbook of metaheuristics, pp. 283–319. Springer, Berlin (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Joaquín Bautista
    • 1
    Email author
  • Rocío Alfaro-Pozo
    • 1
  • Cristina Batalla-García
    • 1
  1. 1.Research Group OPE-PROTHIUSUniversitat Politècnica de CatalunyaBarcelonaSpain

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