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Predator-Prey Techniques for Solving Multiobjective Scheduling Problems for Unrelated Parallel Machines

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Evolutionary Multi-Criterion Optimization (EMO 2017)

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Abstract

The multiobjective scheduling problem on unrelated parallel machines is tackled by predator-prey techniques. Several objectives adopted in the literature were considered and the corresponding best aligned dispatch rules were associated with the predators. Also, we suggest and analyse modifications in the movement operator, in the number of predators, and in the influence of the objectives in the selection/replacement procedures. Numerical comparisons with the popular NSGA-II were performed and good results were obtained by the predator-prey techniques.

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References

  1. Arroyo, J.E.C., de Souza Pereira, A.A.: A GRASP heuristic for the multi-objective permutation flowshop scheduling problem. Intl. J. Adv. Manufact. Technol. 55(5–8), 741–753 (2011)

    Article  Google Scholar 

  2. Bank, J., Werner, F.: Heuristic algorithms for unrelated parallel machine scheduling with a common due date, release dates, and linear earliness and tardiness penalties. Math. Comput. Model. 33(4), 363–383 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  3. Barbosa, H.J.C., Bernardino, H.S., Barreto, A.M.S.: Using performance profiles to analyze the results of the 2006 CEC constrained optimization competition. In: Proceedings of the IEEE World Congress on Computational Intelligence (2010)

    Google Scholar 

  4. de CM Nogueira, J.P., Arroyo, J.E.C., Villadiego, H.M.M., Gonçalves, L.B.: Hybrid grasp heuristics to solve an unrelated parallel machine scheduling problem with earliness and tardiness penalties. Electron. Notes Theor. Comput. Sci. 302, 53–72 (2014)

    Article  Google Scholar 

  5. Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley- Interscience Series in Systems and Optimization. Wiley, New York (2001)

    MATH  Google Scholar 

  6. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000). doi:10.1007/3-540-45356-3_83

    Chapter  Google Scholar 

  7. Dolan, E.D., Moré, J.J.: Benchmarking optimization software with performance profiles. Math. Program. 91(2), 201–213 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  8. Etcheverry, G.V., Anzanello, M.A.: Sequenciamento em máquinas paralelas com tempos de setup dependentes da sequência. In: XXXIII Encontro Nacional de Engenharia de Produção (2013)

    Google Scholar 

  9. Fang, K.T., Lin, B.M.: Parallel-machine scheduling to minimize tardiness penalty and power cost. Comput. Ind. Eng. 64(1), 224–234 (2013)

    Article  MathSciNet  Google Scholar 

  10. Grimme, C., Lepping, J., Schwiegelshohn, U.: Multi-criteria scheduling: an agentbased approach for expert knowledge integration. J. Sched. 16(4), 369–383 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  11. Hoogeveen, H.: Multicriteria scheduling. Eur. J. Oper. Res. 167(3), 592–623 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  12. Kayvanfar, V., Aalaei, A., Hosseininia, M., Rajabi, M.: Unrelated parallel machine scheduling problem with sequence dependent setup times. In: International Conference on Industrial Engineering and Operations Management, Bali, pp. 7–9 (2014)

    Google Scholar 

  13. Laumanns, M., Rudolph, G., Schwefel, H.-P.: A spatial predator-prey approach to multi-objective optimization: a preliminary study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 241–249. Springer, Heidelberg (1998). doi:10.1007/BFb0056867

    Chapter  Google Scholar 

  14. Lee, Y.H., Pinedo, M.: Scheduling jobs on parallel machines with sequencedependent setup times. Eur. J. Oper. Res. 100(3), 464–474 (1997)

    Article  MATH  Google Scholar 

  15. Lei, D.: Multi-objective production scheduling: a survey. Intl. J. Adv. Manuf. Technol. 43(9–10), 926–938 (2009)

    Article  Google Scholar 

  16. Lin, B., Wu, J.: Bicriteria scheduling in a two-machine permutation flowshop. Intl. J. Prod. Res. 44(12), 2299–2312 (2006)

    Article  MathSciNet  Google Scholar 

  17. Lin, Y.K.: Particle swarm optimization algorithm for unrelated parallel machine scheduling with release dates. Mathematical Problems in Engineering (2013)

    Google Scholar 

  18. MacCarthy, B., Liu, J.: Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling. Intl. J. Prod. Res. 31(1), 59–79 (1993)

    Article  Google Scholar 

  19. de Souza Pereira, A.A.: Análise de objetivos e meta-heurísticas para problemas multiobjetivo de sequenciamento da Produção. Ph.D. thesis. Universidade Federal de Juiz de Fora, Juiz de Fora, Brasil (2016)

    Google Scholar 

  20. de Souza Pereira, A.A., Bernardino, H.S., Barbosa, H.J.C.: Comparação entre abordagens multiobjetivo e mono-objetivo para o problema de programação em máquinas paralelas não relacionadas com tempos de preparação dependentes da sequência e datas de liberação. In: XXI Simpósio de Engenharia de Produção (2014)

    Google Scholar 

  21. Pfund, M., Fowler, J.W., Gupta, J.N.: A survey of algorithms for single and multiobjective unrelated parallel-machine deterministic scheduling problems. J. Chin. Inst. Ind. Eng. 21(3), 230–241 (2004)

    Google Scholar 

  22. Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems, 4th edn. Springer Publishing Company, Incorporated, Heidelberg (2012)

    Book  MATH  Google Scholar 

  23. Ruiz, R., Şerifoğlu, F.S., Urlings, T.: Modeling realistic hybrid flexible flowshop scheduling problems. Comput. Oper. Res. 35(4), 1151–1175 (2008)

    Article  MATH  Google Scholar 

  24. Ruiz, R., Stützle, T.: A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. Eur. J. Oper. Res. 177(3), 2033–2049 (2007)

    Article  MATH  Google Scholar 

  25. Suresh, V., Chaudhuri, D.: Bicriteria scheduling problem for unrelated parallel machines. Comput. Ind. Eng. 30(1), 77–82 (1996)

    Article  Google Scholar 

  26. T’kindt, V., Billaut, J.C., Proust, C.: Solving a bicriteria scheduling problem on unrelated parallel machines occurring in the glass bottle industry. Eur. J. Oper. Res. 135(1), 42–49 (2001)

    Article  MATH  Google Scholar 

  27. Torabi, S.A., Sahebjamnia, N., Mansouri, S.A., Bajestani, M.A.: A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem. Appl. Soft Comput. 13(12), 4750–4762 (2013)

    Article  Google Scholar 

  28. Vallada, E., Ruiz, R.: A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times. Eur. J. Oper. Res. 211(3), 612–622 (2011)

    Article  MathSciNet  Google Scholar 

  29. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank the reviewers for their comments, which helped to improve the quality of the final version, and the support provided by CNPq (grant 310778/2013-1), FAPEMIG (grants APQ-03414-15), and PPGMC/UFJF.

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Authors and Affiliations

  1. Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora, Minas Gerais, Brazil

    Ana Amélia S. Pereira, Helio J. C. Barbosa & Heder S. Bernardino

  2. Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Rio de Janeiro, Brazil

    Helio J. C. Barbosa

Authors
  1. Ana Amélia S. Pereira
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  2. Helio J. C. Barbosa
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  3. Heder S. Bernardino
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Corresponding author

Correspondence to Heder S. Bernardino .

Editor information

Editors and Affiliations

  1. University of Münster, Münster, Germany

    Heike Trautmann

  2. TU Dortmund University, Dortmund, Germany

    Günter Rudolph

  3. University of Wuppertal, Wuppertal, Germany

    Kathrin Klamroth

  4. CINVESTAV-IPN, Mexico City, Mexico

    Oliver Schütze

  5. Clemson University, Clemson, South Carolina, USA

    Margaret Wiecek

  6. University of Surrey, Guildford, United Kingdom

    Yaochu Jin

  7. University of Münster, Münster, Germany

    Christian Grimme

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Pereira, A.A.S., Barbosa, H.J.C., Bernardino, H.S. (2017). Predator-Prey Techniques for Solving Multiobjective Scheduling Problems for Unrelated Parallel Machines. In: Trautmann, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science(), vol 10173. Springer, Cham. https://doi.org/10.1007/978-3-319-54157-0_33

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  • DOI: https://doi.org/10.1007/978-3-319-54157-0_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54156-3

  • Online ISBN: 978-3-319-54157-0

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