Skip to main content

Advertisement

Log in

An improved quantum genetic algorithm based on MAGTD for dynamic FJSP

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

For the purpose of solving the dynamic flexible job-shop scheduling problem, this paper establishes the mathematical model to minimize the makespan and stability value, an improved double chains quantum genetic algorithm was proposed. Firstly, it is proposed that the method of double chains structure coding including machine allocation chain and process chain. Secondly, it is proposed that non- dominated sorting based on the crowding distance selection strategy. Thirdly, the most satisfying solution is obtained through the multi-attribute grey target decision model. Finally, the novel method is applied to the Brandimarte example and a dynamic simulation, the result of comparing with other classical algorithms verifies its effectiveness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Bagheri A, Zandith M, Mahdavi I, Yanzdanni M (2010) An artificial immune algorithm for the flexible job-shop scheduling problem. Future Gener Comp Syst 26(4):533–541

    Article  Google Scholar 

  • Brandimarte P (1993) Routing and scheduling in a flexible job shop by tabu search. Ann Oper Res 41(3):157–183

    Article  MATH  Google Scholar 

  • Bucker P, Schlie R (1990) Job-shop scheduling with multi-purpose machines. Computing 45(4):369–375

    Article  MathSciNet  MATH  Google Scholar 

  • Fattahi P, Fallahi A (2010) Dynamic scheduling in flexible job shop systems by considering simultaneously efficiency and stability. CIRP J Manuf Sci Technol 2(2):114–123

    Article  Google Scholar 

  • Ishibuchi H, Murata T (1998) A multi-objective genetic local search algorithm and its application to flow shop scheduling. IEEE Trans Syst Man Cybern Part C Appl Rev 28(3):392–403

    Article  Google Scholar 

  • Jackson J R (1957) Simulation research on job shop production. Naval Res Logist Q 4(3):287–295

    Article  Google Scholar 

  • Kacem I, Hammadi S, Borne P (2002) Approach by localization and multi-objective evolutionary optimization for flexible job-shop scheduling problems. IEEE Trans Syst Man Cyb C 32(1):1–13

    Article  MATH  Google Scholar 

  • Liu X, Zhang G (2014) Flexible job shop dynamic scheduling method research. Mach Des Manuf 5:250–252+256

    Google Scholar 

  • Liu A, Yang Y, Xing Q et al (2011) Multi-population genetic algorithm in multi-objective fuzzy and flexible Job Shop scheduling Dynamic scheduling on multi-objective flexible Job Shop. Comput Integr Manuf Syst 17(9):1954–1961

    Google Scholar 

  • Liu X, Jiao X, Ning T et al (2014) Improved method of flexible Job Shop scheduling based on double chains quantum genetic algorithm. Comput Integr Manuf Syst 7(12):1–11

    Google Scholar 

  • Ning T, Guo C, Chen R et al (2016a) A novel hybrid method for solving flexible job-shop scheduling problem. Open Cybern Syst J 10:13–19

    Article  Google Scholar 

  • Ning T, Huang M, Liang X et al (2016b) A novel dynamic scheduling strategy for solving flexible job-shop problems. J Ambient Intell Humaniz Comput 25(7):721–729

    Article  Google Scholar 

  • Pezzella F, Morganti G, Ciaschetti G (2008) A genetic algorithm for the flexible job-shop scheduling problem. Comput Oper Res 35(10):3202–3212

    Article  MATH  Google Scholar 

  • Rahmat SHA, Zandieh M, Yazdani M (2013) Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem. Int J Adv Manuf Technol 64(5):915–932

    Article  Google Scholar 

  • Shi J, Jiao H, Chen T (2012) Multi-objective Pareto optimization on flexible job-shop scheduling problem about due punishment. J Mech Eng 48(12):188–196

    Google Scholar 

  • Tang J (2012) Research of scheduling problems based on hybrid evolutionary algorithm. South China University of Technology, Guangzhou

    Google Scholar 

  • Wang S, Zhang C, Liu Q et al (2014) Dynamic scheduling of flexible job shop based on different rescheduling cycle. Comput Integr Manuf Syst 7(14):1–14

    Google Scholar 

  • Yang K, Liu X (2009) Multi-objective optimization for flow shop scheduling with the group technology assumption removed. Comput Integr Manuf Syst 2:348–357

    Google Scholar 

  • Zhang L (2013) Research on theories and methods for dynamic job shop scheduling based on predictive-reactive scheduling. Huazhong University of Science and Technology, Wuhan

  • Zhang J, Wang W, Xu X et al (2012) Hybrid particle-swarm optimization for multi- objective flexible job-shop scheduling problem. Control Theory Appl 29(6):30–37

    MATH  Google Scholar 

  • Zhang L, Gao L, Li X (2013) A hybrid genetic algorithm and tabu search for a multi-objective dynamic job shop scheduling problem. Int J Prod Res 51(12):3516–3531

    Article  Google Scholar 

Download references

Acknowledgements

This work is financially supported by the National Natural Science Foundation, China (No. 51579024), Liaoning Provincial Natural Science Foundation of China (No. 201602131), Dr scientific research fund of Liaoning Province (No. 201601244), Liaoning Provincial Social Science Planning Foundation of China (No. L16BGL008) and Dalian Social Science Foundation of China (2016dlskyb104).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tao Ning.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ning, T., Jin, H., Song, X. et al. An improved quantum genetic algorithm based on MAGTD for dynamic FJSP. J Ambient Intell Human Comput 9, 931–940 (2018). https://doi.org/10.1007/s12652-017-0486-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-017-0486-4

Keywords

Navigation