Differential Evolution Based Hyper-heuristic for the Flexible Job-Shop Scheduling Problem with Fuzzy Processing Time

  • Jian Lin
  • Dike Luo
  • Xiaodong Li
  • Kaizhou Gao
  • Yanan Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10593)

Abstract

In this paper, a differential evolution based hyper-heuristic (DEHH) algorithm is proposed to solve the flexible job-shop scheduling problem with fuzzy processing time (FJSPF). In the DEHH scheme, five simple and effective heuristic rules are designed to construct a set of low-level heuristics, and differential evolution is employed as the high-level strategy to manipulate the low-level heuristics to operate on the solution domain. Additionally, an efficient hybrid machine assignment scheme is proposed to decode a solution to a feasible schedule. The effectiveness of the DEHH is evaluated on two typical benchmark sets and the computational results indicate the superiority of the proposed hyper-heuristic scheme over the state-of-the-art algorithms.

Keywords

Differential evolution Hyper-heuristic Flexible job-shop scheduling Fuzzy processing time Solution decoding 

Notes

Acknowledgment

This research is part of a project supported by the Zhejiang Provincial Natural Science Foundation of China (Grant nos. LQ15F030002 and LY15F020014), the National Natural Science Foundation of China (Grant nos. 61503331, 71671160 and 61603169), the National Undergraduate Training Programs for Innovation and Entrepreneurship (201611482012) and the Zhejiang Key Laboratory of Solid State Drive and Data Security (Grant No. 2015E10003).

References

  1. 1.
    Wang, L., Zhou, G., Xu, Y., Wang, S.Y., Liu, M.: An effective artificial bee colony algorithm for the flexible job-shop scheduling problem. Int. J. Adv. Manuf. Technol. 60, 303–315 (2012)CrossRefGoogle Scholar
  2. 2.
    Logendran, R., Sonthinen, A.: A Tabu search-based approach for scheduling job-shop type flexible manufacturing systems. J. Oper. Res. Soc. 48, 264–277 (1997)CrossRefMATHGoogle Scholar
  3. 3.
    Gomes, M.C., Barbosa-Povoa, A.P., Novais, A.Q.: Optimal scheduling for flexible job shop operation. Int. J. Prod. Res. 43, 2323–2353 (2005)CrossRefMATHGoogle Scholar
  4. 4.
    Saidi-Mehrabad, M., Fattahi, P.: Flexible job shop scheduling with tabu search algorithms. Int. J. Adv. Manuf. Technol. 32, 563–570 (2007)CrossRefGoogle Scholar
  5. 5.
    Chen, H.X., Ihlow, J., Lehmann, C.: A genetic algorithm for flexible job-shop scheduling. In: 1999 IEEE International Conference on Robotics and Automation, Detroit, MI, USA, pp. 1120–1125. IEEE (1999)Google Scholar
  6. 6.
    Pezzella, F., Morganti, G., Ciaschetti, G.: A genetic algorithm for the flexible job-shop scheduling problem. Comput. Oper. Res. 35, 3202–3212 (2008)CrossRefMATHGoogle Scholar
  7. 7.
    Gutierrez, C., Garcia-Magarino, I.: Modular design of a hybrid genetic algorithm for a flexible job-shop scheduling problem. Knowl.-Based Syst. 24, 102–112 (2011)CrossRefGoogle Scholar
  8. 8.
    Gholami, M., Zandieh, M.: Integrating simulation and genetic algorithm to schedule a dynamic flexible job shop. J. Intell. Manuf. 20, 481–498 (2009)CrossRefGoogle Scholar
  9. 9.
    Karimi, H., Rahmati, S.H.A., Zandieh, M.: An efficient knowledge-based algorithm for the flexible job shop scheduling problem. Knowl.-Based Syst. 36, 236–244 (2012)CrossRefGoogle Scholar
  10. 10.
    Yuan, Y., Xu, H.: Flexible job shop scheduling using hybrid differential evolution algorithms. Comput. Ind. Eng. 65, 246–260 (2013)CrossRefGoogle Scholar
  11. 11.
    Rossi, A.: Flexible job shop scheduling with sequence-dependent setup and transportation times by ant colony with reinforced pheromone relationships. Int. J. Prod. Econ. 153, 253–267 (2014)CrossRefGoogle Scholar
  12. 12.
    Ziaee, M.: A heuristic algorithm for solving flexible job shop scheduling problem. Int. J. Adv. Manuf. Technol. 71, 519–528 (2014)CrossRefGoogle Scholar
  13. 13.
    Sakawa, M., Kubota, R.: Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms. Eur. J. Oper. Res. 120, 393–407 (2000)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Gao, K.Z., Suganthan, P.N., Pan, Q.K., Tasgetiren, M.F.: An effective discrete harmony search algorithm for flexible job shop scheduling problem with fuzzy processing time. Int. J. Prod. Res. 53, 5896–5911 (2015)CrossRefGoogle Scholar
  15. 15.
    Gao, K.Z., Suganthan, P.N., Pan, Q.K., Chua, T.J., Chong, C.S., Cai, T.X.: An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time. Expert Syst. Appl. 65, 52–67 (2016)CrossRefGoogle Scholar
  16. 16.
    Lei, D.M.: A genetic algorithm for flexible job shop scheduling with fuzzy processing time. Int. J. Prod. Res. 48, 2995–3013 (2010)CrossRefMATHGoogle Scholar
  17. 17.
    Lei, D.M.: Co-evolutionary genetic algorithm for fuzzy flexible job shop scheduling. Appl. Soft Comput. 12, 2237–2245 (2012)CrossRefGoogle Scholar
  18. 18.
    Wang, L., Zhou, G., Xu, Y., Liu, M.: A hybrid artificial bee colony algorithm for the fuzzy flexible job-shop scheduling problem. Int. J. Prod. Res. 51, 3593–3608 (2013)CrossRefGoogle Scholar
  19. 19.
    Wang, S., Wang, L., Xu, Y., Liu, M.: An effective estimation of distribution algorithm for the flexible job-shop scheduling problem with fuzzy processing time. Int. J. Prod. Res. 51, 3778–3793 (2013)CrossRefGoogle Scholar
  20. 20.
    Xu, Y., Wang, L., Wang, S.Y., Liu, M.: An effective teaching-learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time. Neurocomputing 148, 260–268 (2015)CrossRefGoogle Scholar
  21. 21.
    Lin, J.: A hybrid biogeography-based optimization for the fuzzy flexible job-shop scheduling problem. Knowl.-Based Syst. 78, 59–74 (2015)CrossRefGoogle Scholar
  22. 22.
    Koulinas, G., Kotsikas, L., Anagnostopoulos, K.: A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem. Inf. Sci. 277, 680–693 (2014)CrossRefGoogle Scholar
  23. 23.
    Salcedo-Sanz, S., Matías-Román, J.M., Jiménez-Fernández, S., Portilla-Figueras, A., Cuadra, L.: An evolutionary-based hyper-heuristic approach for the Jawbreaker puzzle. Applied Intelligence 40, 404–414 (2014)CrossRefGoogle Scholar
  24. 24.
    Gascón-Moreno, J., Salcedo-Sanz, S., Saavedra-Moreno, B., Carro-Calvo, L., Portilla-Figueras, A.: An evolutionary-based hyper-heuristic approach for optimal construction of group method of data handling networks. Inf. Sci. 247, 94–108 (2013)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Anwar, K., Khader, A.T., Al-Betar, M.A., Awadallah, M.A.: Harmony search-based hyper-heuristic for examination timetabling. In: 2013 IEEE 9th International Colloquium on Signal Processing and its Applications, Kuala Lumpur, Malaysia, pp. 176–181. IEEE (2013)Google Scholar
  26. 26.
    Lin, J., Wang, Z.-J., Li, X.: A backtracking search hyper-heuristic for the distributed assembly flow-shop scheduling problem. Swarm Evol. Comput. 36, 124–135 (2017)CrossRefGoogle Scholar
  27. 27.
    Rajni, Chana, I.: Bacterial foraging based hyper-heuristic for resource scheduling in grid computing. Future Gener. Comput. Syst. 29, 751–762 (2014)CrossRefGoogle Scholar
  28. 28.
    Ouelhadj, D., Petrovic, S.: A cooperative hyper-heuristic search framework. J. Heuristics 16, 835–857 (2010)CrossRefMATHGoogle Scholar
  29. 29.
    Hart, E., Sim, K.: A hyper-heuristic ensemble method for static job-shop scheduling. Evol. Comput. 24, 609–635 (2016)CrossRefGoogle Scholar
  30. 30.
    Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)MathSciNetCrossRefMATHGoogle Scholar
  31. 31.
    Bortolan, G., Degani, R.: A review of some methods for ranking fuzzy subsets. Fuzzy Sets Syst. 15, 1–19 (1985)MathSciNetCrossRefMATHGoogle Scholar
  32. 32.
    Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15, 4–31 (2011)CrossRefGoogle Scholar
  33. 33.
    Gao, J., Sun, L.Y., Gen, M.: A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Comput. Oper. Res. 35, 2892–2907 (2008)MathSciNetCrossRefMATHGoogle Scholar
  34. 34.
    Boussaid, I., Chatterjee, A., Siarry, P., Ahmed-Nacer, M.: Biogeography-based optimization for constrained optimization problems. Comput. Oper. Res. 39, 3293–3304 (2012)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jian Lin
    • 1
  • Dike Luo
    • 1
  • Xiaodong Li
    • 2
  • Kaizhou Gao
    • 3
  • Yanan Liu
    • 1
  1. 1.School of InformationZhejiang University of Finance and EconomicsHangzhouChina
  2. 2.School of Science (Computer Science and IT)RMIT UniversityMelbourneAustralia
  3. 3.School of ComputerLiaocheng UniversityLiaochengChina

Personalised recommendations