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Multi-objective Flexible Job Shop Scheduling Problem with Energy Consumption Constraint Using Imperialist Competitive Algorithm

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Intelligent Computing Theories and Application (ICIC 2018)

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Abstract

In this paper, multi-objective flexible job shop scheduling problem (MOFJSP) with energy consumption constraint is investigated and a novel imperialist competitive algorithm (ICA) is used to optimize makespan and total tardiness under a constraint that total energy consumption doesn’t exceed a given threshold. The flow of ICA consists of two parts. In the first part, a MOFJSP is obtained by adding total energy consumption as objective and optimized, all generated feasible solutions are stored and updated to build a population of the second part; in the second part, the original MOFJSP is solved by starting with the population. New strategies are applied to build initial empires twice to adapt to the two-part structure and imperialist’s reinforced search is added. The computational results show that the new approach to constraint is effective and ICA is a very competitive algorithm.

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References

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  3. Yuan, Y., Xu, H.: Multiobjective flexible job shop scheduling using memetic algorithms. IEEE Trans. Autom. Sci. Eng. 12(1), 336–353 (2015)

    Article  Google Scholar 

  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. Manuf. Technol. 77(1), 51–66 (2015)

    Article  Google Scholar 

  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)

    MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 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. Lei, D.M.: Simplified multi-objective genetic algorithm for stochastic job shop scheduling. Appl. Soft Comput. 11(8), 4991–4996 (2011)

    Article  Google Scholar 

  25. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(1), 182–197 (2002)

    Article  Google Scholar 

  26. Atashpaz-Gagari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialist competition. In: IEEE Congress on Evolutionary Computation, Singapore, pp. 4661–4667 (2007)

    Google Scholar 

  27. Goldansaz, S.M., Jolai, F., Anaraki, A.H.Z.: A hybrid imperialist competitive algorithm for minimizing makespan in a multi-processor open shop. Appl. Math. Model. 37(23), 9603–9616 (2013)

    Article  MathSciNet  Google Scholar 

  28. Naderi, B., Yazdani, M.: A model and imperialist competitive algorithm for hybrid flow shops with sublots and setup times. J. Manuf. Syst. 33(4), 647–653 (2014)

    Article  Google Scholar 

  29. Ghasemishabankareh, B., Shahsavari-Pour, N., Basiri, M.A., Li, X.D.: A hybrid imperialist competitive algorithm for the flexible job shop problem. Ray, T., et al. (eds.) ACALCI 2016, LNAI 9592, pp. 221–233 (2016)

    Google Scholar 

  30. Brandimarte, P.: Routing and scheduling in a flexible job shop by tabu search. Ann. Oper. Res. 41(1), 157–183 (1993)

    Article  Google Scholar 

  31. 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 

  32. Zitzler, E., Thiele, L.: Multi-objective 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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Acknowledgement

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

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Authors and Affiliations

  1. School of Automation, Wuhan University of Technology, Wuhan, 430070, China

    Chengzhi Guo & Deming Lei

Authors
  1. Chengzhi Guo
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  2. Deming Lei
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Corresponding author

Correspondence to Deming Lei .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytechnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology Kanpur, Kanpur, India

    Phalguni Gupta

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Guo, C., Lei, D. (2018). Multi-objective Flexible Job Shop Scheduling Problem with Energy Consumption Constraint Using Imperialist Competitive Algorithm. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_66

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  • DOI: https://doi.org/10.1007/978-3-319-95930-6_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95929-0

  • Online ISBN: 978-3-319-95930-6

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