Skip to main content
SpringerLink
Log in

Parallel hybrid heuristics for the permutation flow shop problem

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

This paper addresses the Permutation Flowshop Problem with minimization of makespan, which is denoted by Fm|prmu|C max. In the permutational scenario, the sequence of jobs has to remain the same in all machines. The Flowshop Problem (FSP) is known to be NP-hard when more than three machines are considered. Thus, for medium and large scale instances, high-quality heuristics are needed to find good solutions in reasonable time. We propose and analyse parallel hybrid search methods that fully use the computational power of current multi-core machines. The parallel methods combine a memetic algorithm (MA) and several iterated greedy algorithms (IG) running concurrently. Two test scenarios were included, with short and long CPU times. The tests were conducted on the set of benchmark instances introduced by Taillard (Eur. J. Oper. Res. 64:278–285, 1993), commonly used to assess the performance of new methods. Results indicate that the use of the MA to manage a pool of solutions is highly effective, allowing the improvement of the best known upper bound for one of the instances.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Agarwal, A., Colak, S., & Eryarsoy, E. (2006). Improvement heuristic for the flow-shop scheduling problem: an adaptive-learning approach. European Journal of Operational Research, 169, 801–815.

    Article  Google Scholar 

  • Baker, K. R. (1993). Handbooks in operations research and management science: Vol. 4. Requirements planning (pp. 571–627). New York: Elsevier.

    Google Scholar 

  • Ben-Daya, M., & Al-Fawzan, M. (1998). A tabu search approach for the flow shop scheduling problem. European Journal of Operational Research, 109, 88–95.

    Article  Google Scholar 

  • Buriol, L., Franca, P., & Moscato, P. (2004). A new memetic algorithm for the asymmetric traveling salesman problem. Journal of Heuristics, 10, 483–506.

    Article  Google Scholar 

  • Coffman, E. (1976). Computer and job-shop scheduling theory. New York: Wiley.

    Google Scholar 

  • Dannenbring, D. G. (1977). An evaluation of flow shop sequencing heuristics. Management Science, 23, 1174–1182.

    Article  Google Scholar 

  • Glover, F., & Kochenberger, G. (2003). Handbook of metaheuristics. New York: Springer.

    Google Scholar 

  • Goldberg, D., & Sastry, K. (2010). Genetic algorithms: the design of innovation (2nd ed.). New York: Springer.

    Google Scholar 

  • Johnson, S. M. (1954). Optimal two- and three-stage production schedules with setup times included. Naval Research Logistics Quarterly, 1, 61–68.

    Article  Google Scholar 

  • Moscato, P., Mendes, A., & Berretta, R. (2007). Benchmarking a memetic algorithm for ordering microarray data. Biosystems, 88, 56–75.

    Article  Google Scholar 

  • Nagano, M. S., & Moccellin, J. V. (2002). A high quality solution constructive heuristic for flow shop sequencing. The Journal of the Operational Research Society, 53, 1374–1379.

    Article  Google Scholar 

  • Nawaz, M., Enscore, E. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, 11, 91–95.

    Article  Google Scholar 

  • Ogbu, F. A., & Smith, D. K. (1990). The application of the simulated annealing to the solution of the n/m/c max flow shop problem. Computers & Operations Research, 17, 243–253.

    Article  Google Scholar 

  • Reeves, C. R. (1995). A genetic algorithm for flowshop sequencing. Computers & Operations Research, 22, 5–13.

    Article  Google Scholar 

  • Rinnooy Kan, A. H. G. (1976). Machine scheduling problems: classification, complexity, and computations. The Hagues: Nijhoff.

    Google Scholar 

  • Ruiz, R., & Maroto, C. (2005). A comprehensive review and evaluation of permutation flowshop heuristics. European Journal of Operational Research, 165, 479–494.

    Article  Google Scholar 

  • Ruiz, R., & Stützle, T. (2006). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 77, 2033–2049.

    Google Scholar 

  • Ruiz, R., Maroto, C., & Alcaraz, J. (2003). New genetic algorithms for the permutational flowshop scheduling problem. In MIC2003: The fifth metaheuristics international conference (pp. 63.1–63.8).

    Google Scholar 

  • Ruiz, R., Maroto, C., & Alcaraz, J. (2006). Two new robust genetic algorithms for the flowshop scheduling problem. Omega, 34, 461–476.

    Article  Google Scholar 

  • Stützle, T. (1998). Applying iterated local search to the permutation flow shop problem. Technical report, TU Darmstadt, AIDA-98-04, FG Intellektik.

  • Taillard, É. (1990). Some efficient heuristic methods for the flow shop sequencing problem. European Journal of Operational Research, 47, 65–74.

    Article  Google Scholar 

  • Taillard, É. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64, 278–285.

    Article  Google Scholar 

  • Vallada, E., & Ruiz, R. (2009). Cooperative metaheuristics for the permutation flowshop scheduling problem. European Journal of Operational Research, 192, 365–376.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departamento de Engenharia de Produção, Universidade Federal de Minas Gerais (UFMG), Av. Antonio Carlos, 6627, Belo Horizonte, Brazil

    Martín Gómez Ravetti

  2. School of Electrical Engineering and Computer Science, The University of Newcastle, Newcastle, NSW, Australia

    Carlos Riveros & Alexandre Mendes

  3. AT&T Labs Research, Florham Park, NJ, 07932-0971, USA

    Mauricio G. C. Resende

  4. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  5. Laboratory of Algorithms and Technologies for Networks Analysis (LATNA), Higher School of Economics, National Research University, 20 Myasnitskaya st., Moscow, 101000, Russia

    Panos M. Pardalos

Authors
  1. Martín Gómez Ravetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlos Riveros
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexandre Mendes
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mauricio G. C. Resende
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Panos M. Pardalos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Martín Gómez Ravetti.

Additional information

Research supported by FAPEMIG, CNPq, NSF and Air Force Grants.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ravetti, M.G., Riveros, C., Mendes, A. et al. Parallel hybrid heuristics for the permutation flow shop problem. Ann Oper Res 199, 269–284 (2012). https://doi.org/10.1007/s10479-011-1056-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-011-1056-3

Keywords

Search

Navigation