Grid Branch-and-Bound for Permutation Flowshop

  • Maciej Drozdowski
  • Paweł Marciniak
  • Grzegorz Pawlak
  • Maciej Płaza
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7204)


Flowshop is an example of a classic hard combinatorial problem. Branch-and-bound is a technique commonly used for solving such hard problems. Together, the two can be used as a benchmark of maturity of parallel processing environment. Grid systems pose a number of hurdles which must be overcome in practical applications. We give a report on applying parallel branch-and-bound for flowshop in grid environment. Methods dealing with the complexities of the environment and the application are proposed, and evaluated.


branch-and-bound flowshop grid computing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bąk, S., Błażewicz, J., Pawlak, G., Płaza, M., Burke, E., Kendall, G.: A parallel branch-and-bound approach to the rectangular guillotine strip cutting problem. INFORMS J. on Computing 23, 15–25 (2011)CrossRefGoogle Scholar
  2. 2.
    Clausen, J.: Branch and bound algorithms - principles and examples, Technical Report, Department of Computer Science, University of Copenhagen (1999)Google Scholar
  3. 3.
    Crainic, T., Le Cun, B., Roucairol, C.: Parallel Branch-and-Bound Algorithms. In: Talbi, E.-G. (ed.) Parallel Combinatorial Optimization, pp. 1–28. John Wiley & Sons (2006)Google Scholar
  4. 4.
  5. 5.
    Garey, M., Johnson, D., Sethi, R.: The complexity of flowshop and jobshop scheduling. Mathematics of Operations Research 1, 117–129 (1976)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Hejazi, S., Saghafian, S.: Flowshop-scheduling problems with makespan criterion: a review. International Journal of Production Research 43, 2895–2929 (2005)zbMATHCrossRefGoogle Scholar
  7. 7.
    Horn, J.: Bibliography on parallel branch-and-bound algorithms (1992),
  8. 8.
    Johnson, S.M.: Optimal two-and-three-stage production schedules with set-up times included. Naval Research Logistics Quarterly 1, 61–68 (1954)CrossRefGoogle Scholar
  9. 9.
    Iyer, S., Saxena, B.: Improved genetic algorithm for the permutation flowshop scheduling problem. Computers & Operations Research 31, 593–606 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Kohler, W., Steiglitz, K.: Enumerative and iterative computational approaches. In: Coffman Jr., E.G. (ed.) Computer and Job-Shop Scheduling Theory, pp. 229–287. Wiley, New York (1976)Google Scholar
  11. 11.
    Lai, T.-H., Sahni, S.: Anomalies in parallel branch-and-bound algorithms. Communications of the ACM 27, 594–602 (1984)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Lai, T.-H., Sprague, A.: Performance of Parallel Branch-and-Bound Algorithms. IEEE Transactions on Computers 34, 962–964 (1985)zbMATHGoogle Scholar
  13. 13.
    Nawaz, M., Enscore, E., Ham, I.: A heuristic algorithm for the m-machine, n-job flowshop sequencing problem. Omega 11, 91–95 (1983)CrossRefGoogle Scholar
  14. 14.
    Reeves, C., Yamada, T.: Genetic algorithms, path relinking, and flowshop sequencing problem. Evolutionary Computation 6, 45–60 (1998)CrossRefGoogle Scholar
  15. 15.
    ProActive - Professional Open Source Middleware for Parallel, Distributed, Multi- core Programming,
  16. 16.
    Taillard, E.: Benchmarks for basic scheduling problems. European Journal of Operational Research 64, 278–285 (1993)zbMATHCrossRefGoogle Scholar
  17. 17.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Maciej Drozdowski
    • 1
  • Paweł Marciniak
    • 1
  • Grzegorz Pawlak
    • 1
  • Maciej Płaza
    • 1
  1. 1.Institute of Computing SciencePoznań University of TechnologyPoznańPoland

Personalised recommendations