Skip to main content

An Evolutionary Multi-task Genetic Algorithm with Assisted-Task for Flexible Job Shop Scheduling

  • Conference paper
  • First Online:
Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1682))

Abstract

Flexible job-shop scheduling problem (FJSP) has aroused much attention from academia. It is known that evolutionary multitasking optimization (EMTO) is famous for solving multiple tasks simultaneously by leveraging the knowledge among tasks. To explore the universality of EMTO, an assisted-task based evolutionary multi-task genetic algorithm (MTGAA) is firstly proposed to deal with FJSP. In MTGAA, each FJSP task is equipped with a constitutive assisted task that generates a high-quality initial population according priory rules, so that the target-task is improved by using the knowledge from assisted-task. For the purpose to improve the ability of searching optimal of MTGAA, an adaptive crossover strategy is designed by using two popular crossover operators at the same time in this paper. Besides, the effectiveness of proposed two components are verified by comparing MTGAA to four variants of MTGAA. The expert mental results of MTGAA are compared with two latest algorithms on standard benchmark data instances and the experimental results show that MTGAA is competitive in dealing with FJSP.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

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

    Article  MATH  Google Scholar 

  2. Gao, K., Yang, F., Zhou, M., Pan, Q., Suganthan, P.N.: Flexible job-shop rescheduling for new job insertion by using discrete Jaya algorithm. IEEE Trans. Cybern. 49, 1944–1955 (2018)

    Article  Google Scholar 

  3. Garey, M.R., Johnson, D.S., Sethi, R.: The complexity of flowshop and jobshop scheduling. Math. Oper. Res. 1, 117–129 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  4. Zhang, G., Shao, X., Li, P., Gao, L.: An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Comput. Ind. Eng. 56, 1309–1318 (2009)

    Article  Google Scholar 

  5. Xing, L.N., Chen, Y.W., Wang, P., Zhao, Q.S., Xiong, J.: A knowledge-based ant colony optimization for flexible job shop scheduling problems. Appl. Soft Comput. 10, 888–896 (2010)

    Article  Google Scholar 

  6. Li, J.Q., Pan, Q.K., Gao, K.Z.: Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. Int. J. Adv. Manuf. Technol. 55, 1159–1169 (2011)

    Article  Google Scholar 

  7. Caldeira, R.H., Gnanavelbabu, A., JosephSolomon, J.: Solving the flexible job shop scheduling problem using a hybrid artificial bee colony algorithm. In: Vijayan, S., NachiappanSubramanian, K. (eds.) Trends in Manufacturing and Engineering Management. LNME, pp. 833–843. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-4745-4_72

    Chapter  Google Scholar 

  8. Zhao, H., et al.: Local binary pattern-based adaptive differential evolution for multimodal optimization problems. IEEE Trans. Cybern. 50, 3343–3357 (2019)

    Article  Google Scholar 

  9. Zhao, H., Li, J., Liu, J.: Localized distance and time-based differential evolution for multimodal optimization problems. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 510–513 (2022)

    Google Scholar 

  10. Zhao, H., Chen, Z.-G., Zhan, Z.-H., Kwong, S., Zhang, J.: Multiple populations co-evolutionary particle swarm optimization for multi-objective cardinality constrained portfolio optimization problem. Neurocomputing 430, 58–70 (2021)

    Article  Google Scholar 

  11. Gao, K., Cao, Z., Zhang, L., Chen, Z., Han, Y., Pan, Q.: A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems. IEEE/CAA J. Autom. Sin. 6, 904–916 (2019)

    Article  Google Scholar 

  12. Shao, G., Shangguan, Y., Tao, J., Zheng, J., Liu, T., Wen, Y.: An improved genetic algorithm for structural optimization of Au–Ag bimetallic nanoparticles. Appl. Soft Comput. 73, 39–49 (2018)

    Article  Google Scholar 

  13. Zhang, G., Gao, L., Shi, Y.: An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Syst. Appl. 38, 3563–3573 (2011)

    Article  Google Scholar 

  14. Li, X.Y., Gao, L.: An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem. Int. J. Prod. Econ. 174, 93–110 (2016)

    Article  Google Scholar 

  15. Gao, J., Sun, L., Gen, M.: A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Comput. Oper. Res. 35, 2892–2907 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  16. Chen, R., Yang, B., Li, S., Wang, S.: A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem. Comput. Ind. Eng. 149, 106778 (2020)

    Article  Google Scholar 

  17. Gupta, A., Ong, Y.-S., Feng, L.: Multifactorial evolution: toward evolutionary multitasking. IEEE Trans. Evol. Comput. 20, 343–357 (2015)

    Article  Google Scholar 

  18. Wei, T., Wang, S., Zhong, J., Liu, D., Zhang, J.: A review on evolutionary multi-task optimization: trends and challenges. IEEE Trans. Evol. Comput. 26, 941–960 (2021). https://doi.org/10.1109/TEVC.2021.3139437

    Article  Google Scholar 

  19. Osaba, E., Del Ser, J., Martinez, A.D., Hussain, A.: Evolutionary multitask optimization: a methodological overview, challenges, and future research directions. Cogn. Comput. 14, 927–954 (2022)

    Article  Google Scholar 

  20. Zhang, F., Mei, Y., Nguyen, S., Zhang, M., Tan, K.C.: Surrogate-assisted evolutionary multitask genetic programming for dynamic flexible job shop scheduling. IEEE Trans. Evol. Comput. 25, 651–665 (2021)

    Article  Google Scholar 

  21. Yuan, Y., Ong, Y.S., Gupta, A., Tan, P.S., Xu, H.: Evolutionary multitasking in permutation-based combinatorial optimization problems: realization with TSP, QAP, LOP, and JSP. In: 2016 IEEE Region 10 Conference (TENCON), pp. 3157–3164. IEEE (2016)

    Google Scholar 

  22. Davis, L.: Applying adaptive algorithms to epistatic domains. In: IJCAI, pp. 162–164 (1985)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  24. Bagheri, A., Zandieh, M., Mahdavi, I., Yazdani, M.: An artificial immune algorithm for the flexible job-shop scheduling problem. Future Gener. Comput. Syst. 26, 533–541 (2010)

    Article  Google Scholar 

  25. Lee, K. M., Yamakawa, T., Lee, K.-M.: A genetic algorithm for general machine scheduling problems. In: 1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES 1998 (Cat. No. 98EX111), pp. 60–66. IEEE (1998)

    Google Scholar 

  26. Meng, T., Pan, Q.-K., Sang, H.-Y.: A hybrid artificial bee colony algorithm for a flexible job shop scheduling problem with overlapping in operations. Int. J. Prod. Res. 56, 5278–5292 (2018)

    Article  Google Scholar 

  27. Nouiri, M., Bekrar, A., Jemai, A., Niar, S., Ammari, A.C.: An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. J. Intell. Manuf. 29(3), 603–615 (2015). https://doi.org/10.1007/s10845-015-1039-3

    Article  Google Scholar 

  28. Liao, P., Sun, C., Zhang, G., Jin, Y.: Multi-surrogate multi-tasking optimization of expensive problems. Knowl. Based Syst. 205, 106262 (2020)

    Article  Google Scholar 

  29. Wang, C., Wu, K., Liu, J.: Evolutionary multitasking AUC optimization. arXiv preprint arXiv:2201.01145 (2022)

Download references

Acknowledgement

This work was supported by the Guangdong Basic and Applied Basic Research Foundation (2021A151511073, 2022A1515011297).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hong Zhao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ning, X., Zhao, H., Liu, X., Liu, J. (2023). An Evolutionary Multi-task Genetic Algorithm with Assisted-Task for Flexible Job Shop Scheduling. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1682. Springer, Singapore. https://doi.org/10.1007/978-981-99-2385-4_27

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-2385-4_27

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-2384-7

  • Online ISBN: 978-981-99-2385-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics