Advertisement

A Two-Phase Variable Neighborhood Search for Flexible Job Shop Scheduling Problem with Energy Consumption Constraint

  • Chengzhi Guo
  • Deming Lei
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10954)

Abstract

This paper investigates flexible job shop scheduling problem (FJSP) with energy consumption constraint, the goal of which is to minimize makespan and total tardiness under the constraint that total energy consumption doesn’t exceed a given threshold. Energy consumption constraint is not always met and a new method for this constraint is proposed. A two-phase variable neighborhood search (TVNS) is presented. In the first phase, the problem is converted into FJSP with makespan, total tardiness and total energy consumption and a VNS is applied for the new problem. In the second phase, another VNS is for the original problem by strategies for comparing solutions and updating the non-dominated set \( \Omega \) of the first phase. The current solution of TVNS is replaced with a member of \( \Omega \) every a prefixed number of iterations to improve solution quality. Extensive experiments are conducted and computational results validate the effectiveness and advantages of TVNS for the considered FJSP.

Keywords

Flexible job shop scheduling problem Energy consumption constraint Variable neighborhood search 

Notes

Acknowledgement

This work is supported by the National Natural Science of Foundation of China (61573264).

References

  1. 1.
    Kacem, I., Hammadi, S., Borne, P.: Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic. Math. Comput. Simul. 60(3–5), 245–276 (2002)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Gao, J., Gen, M., Sun, L., Zhao, X.: A hybrid of genetic algorithm and bottleneck shifting for multi-objective flexible job shop scheduling problems. Comput. Ind. Eng. 53(1), 149–162 (2007)CrossRefGoogle Scholar
  3. 3.
    Yuan, Y., Xu, H.: Multiobjective flexible job shop scheduling using memetic algorithms. IEEE Trans. Autom. Sci. Eng. 12(1), 336–353 (2015)CrossRefGoogle Scholar
  4. 4.
    Rohaninejad, M., Kheirkhah, A., Fattahi, P., Vahedi-Nouri, B.: A hybrid multi-objective genetic algorithm based on the ELECTRE method for a capacitated flexible job shop scheduling problem. Int. J. Adv. Manufact. Technol. 77(1), 51–66 (2015)CrossRefGoogle Scholar
  5. 5.
    Rohaninejad, M., Sahraeian, R., Nouri, B.V.: Multi-objective optimization of integrated lot-sizing and scheduling problem in flexible job shop. PAIRO Oper. Res. 50(3), 587–609 (2015)MathSciNetMATHGoogle Scholar
  6. 6.
    Li, J., Huang, Y., Niu, X.: A branch population genetic algorithm for dual-resource constrained job shop scheduling problem. Comput. Ind. Eng. 102(1), 113–131 (2016)CrossRefGoogle Scholar
  7. 7.
    Ahmadi, E., Zandieh, M., Farrokh, M., Emami, S.M.: A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithm. Comput. Oper. Res. 73(1), 56–66 (2016)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Shen, X.N., Han, Y., Fu, J.Z.: Robustness measures and robust scheduling for multi-objective stochastic flexible job shop scheduling problems. Soft Comput. (2018, in press)Google Scholar
  9. 9.
    Moslehi, G., Mahnam, M.: A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. Int. J. Prod. Econ. 129(1), 14–22 (2011)CrossRefGoogle Scholar
  10. 10.
    Singh, M.R., Singh, M., Mahapatra, S.S., Jagadev, N.: Particle swarm optimization algorithm embedded with maximum deviation theory for solving multi-objective flexible job shop scheduling problem. Int. J. Adv. Manuf. Technol. 85(9), 2353–2366 (2016)CrossRefGoogle Scholar
  11. 11.
    Gao, K.Z., Suganthan, P.N., Pan, Q.K., Chua, T.J., Cai, T.X., Chong, C.S.: Pareto-based grouping discrete harmony search algorithm for multi-objective flexible job shop scheduling. Inf. Sci. 289(1), 76–90 (2014)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Li, J.Q., Pan, Q.K., Tasgetiren, M.F.: A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance. Appl. Math. Model. 38(3), 1111–1132 (2014)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Jia, S., Hu, Z.H.: Path-relinking tabu search for the multi-objective flexible job shop scheduling problem. Comput. Oper. Res. 47(1), 11–26 (2014)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Bagheri, A., Zandieh, M.: Bi-criteria flexible job-shop scheduling with sequence-dependent setup times-variable neighborhood search approach. J. Manuf. Syst. 30(1), 8–15 (2011)CrossRefGoogle Scholar
  15. 15.
    Li, J.Q., Pan, Q.K., Xie, S.X.: An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems. Appl. Math. Comput. 218(18), 9353–9371 (2012)MathSciNetMATHGoogle Scholar
  16. 16.
    Wang, L., Wang, S.Y., Liu, M.: A Pareto-based estimation of distribution algorithm for the multi-objective flexible job-shop scheduling problem. Int. J. Prod. Res. 51(12), 3574–3592 (2013)CrossRefGoogle Scholar
  17. 17.
    Li, J.Q., Sang, H.Y., Han, Y.Y., Wang, C.G., Gao, K.Z.: Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions. J. Cleaner Prod. 181, 584–598 (2018)CrossRefGoogle Scholar
  18. 18.
    He, Y., Li, Y.F., Wu, T., Sutherland, J.W.: An energy-responsive optimization method for machine tool selection and operation sequence in flexible machining job shops. J. Cleaner Prod. 87(1), 245–254 (2015)CrossRefGoogle Scholar
  19. 19.
    Yin, L.J., Li, X.Y., Gao, L., Lu, C., Zhang, Z.: A novel mathematical model and multi-objective method for the low-carbon flexible job shop scheduling problem. Sustain. Comput. Inf. Syst. 13, 15–30 (2017)Google Scholar
  20. 20.
    Lei, D.M., Zheng, Y.L., Guo, X.P.: 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
  21. 21.
    Mokhtari, H., Hasani, A.: An energy-efficient multi-objective optimization for flexible job shop scheduling. Comput. Ind. Eng. 104, 339–352 (2017)Google Scholar
  22. 22.
    Lei, D.M., Li, M., Wang, L.: A two-phase meta-heuristic for multi-objective flexible job shop scheduling problem with total energy consumption threshold. IEEE Trans. Cybern. (2018, in press)Google Scholar
  23. 23.
    Lei, D.M., Yang, D.J.: Research on flexible job shop scheduling problem with total energy consumption constraint. ACTA Autom. Sinica (2018, in press). (in Chinese)Google Scholar
  24. 24.
    Lei, D.M.: Simplified multi-objective genetic algorithm for stochastic job shop scheduling. Appl. Soft Comput. 11(8), 4991–4996 (2011)CrossRefGoogle Scholar
  25. 25.
    Dauzère-Pérès, S., Paulli, J.: An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search. Ann. Oper. Res. 70(2), 281–306 (1997)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Knowles, J.D., Corne, D.W.: On metrics for comparing non-dominated sets. In: Proceedings of 2002 Congress on Evolutionary Computation, Honolulu, 12–17 May, pp. 711–716 (2002)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of AutomationWuhan University of TechnologyWuhanChina

Personalised recommendations