Progress in Artificial Intelligence

, Volume 6, Issue 2, pp 159–169 | Cite as

Free and regular mixed-model sequences by a linear program-assisted hybrid algorithm GRASP-LP

Regular Paper

Abstract

A linear program-assisted hybrid algorithm (GRASP-LP) is presented to solve a mixed-model sequencing problem in an assembly line. The issue of the problem is to obtain manufacturing sequences of product models with the minimum work overload, allowing the free interruption of operations at workstations and preserving the production mix. The implemented GRASP-LP is compared with other procedures through a case study linked with the Nissan’ Engine Plant from Barcelona.

Keywords

GRASP Linear programming Sequencing Mixed-model assembly lines Production mix preservation 

Notes

Acknowledgements

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

References

  1. 1.
    Bautista, J., Batalla-García, C., Alfaro-Pozo, R.: Models for assembly line balancing by temporal, spatial and ergonomic risk attributes. Eur. J. Oper. Res. 251(3), 814–829 (2016)MathSciNetCrossRefMATHGoogle 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)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Bautista, J., Cano, A.: Solving mixed model sequencing problem in assembly lines with serial workstations with work overload minimisation and interruption rules. Eur. J. Oper. Res. 210(3), 495–513 (2011)CrossRefMATHGoogle Scholar
  4. 4.
    Cano-Belmán, J., Ríos-Mercado, R.Z., Bautista, J.: A scatter search based hyper-heuristic for sequencing a mixed-model assembly line. J. Heuristics 16(6), 749–770 (2010)CrossRefMATHGoogle Scholar
  5. 5.
    Bautista, J., Pereira, J., Adenso-Díaz, B.: A GRASP approach for the extended car sequencing problem. J. Sched. 11, 3–16 (2008)MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Monden, Y.: Toyota Production System: An Integrated Approach to Just-In-Time. Springer, New York (1994)Google Scholar
  7. 7.
    Bautista, J., Alfaro-Pozo, R., Batalla-García, C.: GRASP for sequencing mixed models in an assembly line with work overload, useless time and production regularity. Prog. Artif. Intell. 5(1), 27–33 (2016)CrossRefGoogle Scholar
  8. 8.
    Bautista, J., Cano, A., Alfaro, R., Batalla, C.: Impact of the Production Mix Preservation on the ORV Problem. Advances in Artificial Intelligence, Volume 8109 of the series Lecture Notes in Computer Science, pp. 250–259. Springer, Berlin (2013)Google Scholar
  9. 9.
    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(12), 11001–11010 (2012)CrossRefGoogle Scholar
  10. 10.
    Bautista, J., Cano, A., Alfaro, R.: A hybrid dynamic programming for solving a mixed-model sequencing problem with production mix restriction and free interruptions. Prog. Artif. Intell. (2016). doi: 10.1007/s13748-016-0101-5 Google Scholar
  11. 11.
    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 US (2010)Google Scholar
  12. 12.
    Vanderbei, R.J.: Linear Programming. Foundations and Extensions. International Series in Operations Research & Management Science, vol. 114. Springer, New York (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Research Group OPE-PROTHIUSETSEIB Universitat Politècnica de CatalunyaBarcelonaSpain
  2. 2.EAE Business SchoolBarcelonaSpain

Personalised recommendations