Skip to main content
SpringerLink
Log in

A Multi-objective Particle Swarm Optimization Algorithm Embedded with Maximum Fitness Function for Dual-Resources Constrained Flexible Job Shop Scheduling

  • Conference paper
  • First Online:
Book cover Intelligent Computing Theories and Application (ICIC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12836))

Included in the following conference series:

Abstract

This paper is concerned with a multiple-objective flexible job-shop scheduling problem with dual-resources constraints. Both time and cost-concerned objectives are taken into consideration and the corresponding mathematical model is presented. Based on Maximal fitness function, a hybrid discrete particle swarm algorithm is proposed to effectively solve the problem. The global and local search ability of the algorithm are both improved by modifying the position updating method and simulating annealing strategy with Maximal fitness function. Moreover, external archive is used to reserve better particles. Finally, the effectiveness of the proposed algorithm demonstrated by simulation examples and the results show that the obtained solutions are more uniformly distributed towards the Pareto solutions.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

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

    Article  Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. Xiong, L., Qian, Q., Yunfa, F.: Review of application of genetic algorithms for solving flexible job shop scheduling problems. Comput. Eng. Appl. 55(23), 15–22 (2019)

    Google Scholar 

  4. Elmaraghy, H., Patelb, V., Abdallaha, I.B.: A genetic algorithm based approach for scheduling of dual-resource constrainded manufacturing systems. J. Manuf. Syst. 48(1), 369–372 (1999)

    Google Scholar 

  5. Deb, K.: A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2000)

    Article  Google Scholar 

  6. Lei, D.M.: Multi-objective production scheduling: a survey. Int. J. Adv. Manuf. Technol. 43(9–10), 926–938 (2009)

    Article  Google Scholar 

  7. Shafaei, R., Brunn, P.: Workshop scheduling using practical (inaccurate ) data Part1: the performance of heuristic scheduling rules in a dynamic job shop environment using a rolling time horizon approach. Int. J. Prod. Res. 37(17), 3913–3925 (1999)

    Article  Google Scholar 

  8. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108 (1997)

    Google Scholar 

  9. Balling, R.: The maximin fitness function; multi-objective city and regional planning. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds.) EMO 2003. LNCS, vol. 2632, pp. 1–15. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36970-8_1

    Chapter  MATH  Google Scholar 

  10. Menchaca-Mendez, A., Coello, C.: Selection operators based on maximin fitness function for multi-objective evolutionary algorithms. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds.) EMO 2013. LNCS, vol. 7811, pp. 215–229. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37140-0_19

    Chapter  Google Scholar 

  11. Rajabinasab, A., Mansour, S.: Dynamic flexible job shop scheduling with alternative process plans: an agent-based approach. Int. J. Adv. Manuf. Technol. 54(9–12), 1091–1107 (2011)

    Article  Google Scholar 

  12. 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(1–4), 303–315 (2012)

    Article  Google Scholar 

  13. ElMaraghy, H., Patel, V., Abdallah, I.B.: Scheduling of manufacturing systems under dual-resource constraints using genetic algorithms. J. Manuf. Syst. 19(3), 186–201 (2000)

    Article  Google Scholar 

  14. Li, J.Y., Sun, S.D., Huang, Y.: Adaptive hybrid ant colony optimization for solving dual resource constrained job shop scheduling problem. J. Softw. 6(4), 584–594 (2011)

    Article  Google Scholar 

  15. 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 

  16. Lei, D.M., Guo, X.P.: Variable neighbourhood search for dual-resource constrained flexible job shop scheduling. Int. J. Prod. Res. 52(9), 2519–2529 (2014)

    Article  Google Scholar 

  17. Zheng, X.L., Wang, L.: A knowledge-guided fruit fly optimization algorithm for dual resource constrained flexible job-shop scheduling problem. Int. J. Prod. Res. 54(18), 1–13 (2016)

    Article  Google Scholar 

  18. Li, J., Xie, S., Pan, Q., Wang, S.: A hybrid artificial bee colony algorithm for flexible job shop scheduling problems. Stud. Inform. Control 24(2), 171–180 (2015)

    Google Scholar 

  19. Khalife, M.A., Abbasi, B., Abadi, A.H.K.D.: A simulated annealing algorithm for multi objective flexible job shop scheduling with overlapping in operations. J. Optim. Ind. Eng. 8(8), 1–24 (2015)

    Google Scholar 

  20. Karthikeyan, S., Asokan, P., Nickolas, S.: A hybrid discrete firefly algorithm for solving multi-objective flexible job shop scheduling problems. Int. J. Bio-Inspired Comput. 7(6), 386–401 (2015)

    Article  Google Scholar 

  21. Pan, Q.K.: Multi-objective scheduling optimization of job shop in intelligent manufacturing system, Ph.D. dissertation, Nanjing University of Aeronautics and Astronautics (2003)

    Google Scholar 

  22. Liu, X.X., Xie, L.Y., Tao, Z., Hao, C.Z.: Flexible job shop scheduling for decreasing production costs. J. Northeastern Univ. 29(4), 561–564 (2008)

    MATH  Google Scholar 

  23. Liu, X.X., Cai, G.Y., Xie, L.Y.: Research on bi-objective scheduling optimization for DRC job shop. Modular Mach. Tool Autom. Manuf. Tech. 2009(10), 107–112 (2009)

    Google Scholar 

  24. Li, J.Y., Sun, S.D., Huang, Y., Niu, G.G.: Double- objective inherited genetic algorithm for dual-resource constrained job shop. Control Decis. 26(12), 1761–1767 (2011)

    MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  26. Zhang, J., Wang, W., Xu, X.: A hybrid discrete particle swarm optimization for dual-resource constrained job shop scheduling with resource flexibility. J. Intell. Manuf. 28(8), 1961–1972 (2017). https://doi.org/10.1007/s10845-015-1082-0

  27. Kirkpatrick, S.C.D., Gelatt, J., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  Google Scholar 

  28. Zhang, J., Jie, J., Wang, W., Xu, X.: A hybrid particle swarm optimization for multi-objective flexible job-shop scheduling problem with dual-resources constrained. Int. J. Comput. Sci. Math. 8(6), 526–532 (2017)

    Article  MathSciNet  Google Scholar 

  29. Van Veldhuizen, D.A.: Multiobjective evolutionary algorithms: classifications, analyses, and new innovations. Evol. Comput. 8(2), 125–147 (1999)

    Article  Google Scholar 

  30. Masoud, A., Amir, A.N.: Solving a multi-mode bi-objective resource investment problem using meta- heuristic algorithms. Adv. Comput. Tech. Electromagn. 2015(1), 41–58 (2015)

    Article  Google Scholar 

  31. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)

    Article  Google Scholar 

Download references

Acknowledgment

This research work was partly supported by Natural Science Foundation of Zhejiang Province (Grant No. LGF21G030001) and General Projects of Zhejiang Educational Committee (Grant No. Y201839027).

Author information

Authors and Affiliations

  1. Department of Computer and Information Security, Zhejiang Police College, Hangzhou, Zhejiang, China

    Jing Zhang

  2. College of Automation and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China

    Jing Jie

Authors
  1. Jing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Jie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Jie .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Shenzhen University, Shenzhen, China

    Jianqiang Li

  4. Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia

    Valeriya Gribova

  5. Polytechnic University of Bari, Bari, Italy

    Vitoantonio Bevilacqua

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, J., Jie, J. (2021). A Multi-objective Particle Swarm Optimization Algorithm Embedded with Maximum Fitness Function for Dual-Resources Constrained Flexible Job Shop Scheduling. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Bevilacqua, V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_59

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-84522-3_59

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-84521-6

  • Online ISBN: 978-3-030-84522-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation