Advertisement

Improved Shuffled Frog Leaping Algorithm for Multi-objection Flexible Job-Shop Scheduling Problem

  • Mingli Gou
  • Qingxuan GaoEmail author
  • Su Yang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 924)

Abstract

This paper proposes an improved shuffled frog leaping algorithm (SFLA-PSO) to solve multi-objective flexible job-shop problem. Pareto optimization strategy is used to balance three objectives including minimum the maximum completion time, maximum workload of all machines and the total processing time of all job. In the new algorithm, the particle swarm flight strategy is innovatively embedded into the local update operator of the SFLA-PSO. A dynamic crowding density sorting method is used to update external elite archive which holds the non-dominated population. To further improve the quality of the solution and the diversity of the population, a new local optimization strategy is developed associated with a neighborhood search strategy for achieving a high development ability of the new algorithm. The test results of benchmark instances illustrate the effectiveness of the new algorithm.

Keywords

Flexible job-shop scheduling Multi-objective optimization Pareto optimum solution Shuffled frog leaping algorithm Particle swarm algorithm 

References

  1. 1.
    Garey, M.R., Johnson, D.S., Sethi, R.: The complexity of flowshop and jobshop scheduling. Math. Oper. Res. 1(2), 117–129 (1976)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Deb, K.: A fast and elitist multiobjective genetic algorithm. NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Jadaan, O.A., Rajamani, L., Rao, C.R.: Non-dominated ranked genetic algorithm for solving multi-objective optimization problems, NRGA. J. Theor. Appl. Inf. Technol. 1, 60–67 (2008)Google Scholar
  4. 4.
    Shao, X.Y., Liu, W.Q., Liu, Q.: Hybrid discrete particle swarm optimization for multi-objective flexible job-shop scheduling problem. Int. J. Adv. Manuf. Technol. 67(9–12), 2885–2901 (2013)CrossRefGoogle Scholar
  5. 5.
    Zhong, Y., Yang, H., Mo, R., Sun, H.: Optimization method of flexible job-shop scheduling problem based on niching and particle swarm optimization algorithms. Comput. Integr. Manuf. Syst. 12(21), 3231–3238 (2015)Google Scholar
  6. 6.
    Lei, D., Li, M., Wang, L.: A two-phase meta-heuristic for multiobjective flexible job shop scheduling problem with total energy consumption threshold. IEEE Trans. Cybern. PP(99), 1–13 (2018)Google Scholar
  7. 7.
    Eusuff, M., Lansey, K.: Optimization of water distribution network design using the shuffled frog leaping algorithm. J. Water Resour. Plann. Manage. 129(2003), 210–225 (2003)CrossRefGoogle Scholar
  8. 8.
    Luo, X.H., Yang, Y., Li, X.: Modified shuffled frog-leaping algorithm to solve traveling salesman problem. J. Commun. 30(7), 130–135 (2009)Google Scholar
  9. 9.
    Ren, W.L., Zhao, C.W.: A localization algorithm based on SFLA and PSO for wireless sensor network. Inf. Technol. J. 12(3), 502–505 (2012)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Xu, Y., Wang, L., Liu, M., Wang, S.Y.: An effective shuffled frog-leaping algorithm for hybrid flow-shop scheduling with multiprocessor tasks. Int. J. Adv. Manuf. Technol. 68(5–8), 1529–1537 (2013)CrossRefGoogle Scholar
  11. 11.
    Lei, D., Zheng, Y., Guo, X.: A shuffled frog-leaping algorithm for flexible job shop scheduling with the consideration of energy consumption. Int. J. Prod. Res. 55(11), 3126–3140 (2017)CrossRefGoogle Scholar
  12. 12.
    Kacem, I., Hammadi, S., Borne, P.: Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 32(1), 1–13 (2002)CrossRefGoogle Scholar
  13. 13.
    Xia, W.J., Wu, Z.M.: An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Comput. Ind. Eng. 48(2005), 409–425 (2005)CrossRefGoogle Scholar
  14. 14.
    Zhang, G.H., Shao, X.Y., Li, P.G., Gao, L.: An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Comput. Ind. Eng. 56(2009), 1309–1318 (2009)CrossRefGoogle Scholar
  15. 15.
    Xing, L.N., Chen, Y.W., Yang, K.W.: Multi-objective flexible job shop schedule, design and evaluation by simulation modeling. Appl. Soft Comput. 9(2009), 362–376 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.College of Mechanical EngineeringChongqing UniversityChongqingChina

Personalised recommendations