Skip to main content
SpringerLink
Log in

A Multiobjective Genetic Algorithm for Hybrid Flow Shop of a Harddisk Drive’s Manufacturer

  • Conference paper
  • First Online:
Book cover Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 520))

Abstract

This paper proposes a solution procedure to solve a scheduling problem of multiobjective hybrid flow shops (HFS). The assembly line of magnetic head operation is composed of several stages of processes. It can be classified as HFS. There are many product families utilizing these assembly line. A constraint of this scheduling is that some models have to be operated in specific parallel machines because of capability of machine and quality issues. The optimization approach namely the preemptive goal programming is employed to solve this scheduling problem. Due to the complexity of the problem, the non-dominated sorting genetic algorithm-II (NSGA-II) is used to search for the solution. The comparison between the optimization and metaheuristic (NSGA-II) is provided. It is found that NSGA-II is more effective in terms of computational times and the quality of solutions. The diversity problem of pareto-optimal solutions is also discussed.

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 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover 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. Fadaei, M., Zandieh, M.: Scheduling a bi-objective hybrid flow shop with sequence-dependent family setup times using metaheuristics. Arab. J. Sci. Eng. 38, 2233–2244 (2013)

    Article  Google Scholar 

  2. Nagar, A., Heragu, S.S., Haddock, J.: A branch and bound approach for two-machine flowshop scheduling problem. J. Oper. Res. Soc. 46, 721–734 (1995)

    Article  Google Scholar 

  3. Chou, F.D., Lee, C.E.: Two-machine flowshop scheduling with bicriteria problem. Comput. Ind. Eng. 36, 549–564 (1999)

    Article  Google Scholar 

  4. Allahverdi, A.: A new heuristic for m-machine flowshop scheduling problem with bicriteria of makespan and maximum tardiness. Comput. Oper. Res. 31(2), 157–180 (2004)

    Article  MathSciNet  Google Scholar 

  5. Rajendran, C.: Two-stage flowshop scheduling problem with bicriteria. J. Oper. Res. Soc. 43(9), 871–884 (1992)

    Article  Google Scholar 

  6. Gupta, J.N.D., Hennig, K., Werner, F.: Local search heuristics for two-stage flowshop problems with secondary criterion. Comput. Oper. Res. 29(2), 123–149 (2002)

    Article  Google Scholar 

  7. T’kindt, V., Gupta, J.N.D., Billaut, J.-C.: Two-machine flowshop scheduling with a secondary criterion. Comput. Oper. Res. 30(4), 505–526 (2003)

    Article  Google Scholar 

  8. Özgüven, C., Yavuz, Y., Özbakur, L.: Mixed integer goal programming models for the flexible job-shop scheduling problems with separable and non-separable sequence dependent setup times. Appl. Math. Modell. 36, 846–858 (2012)

    Article  MathSciNet  Google Scholar 

  9. Cho, H.-M., Bae, S.-J., Kim, J., Jeong, I.-J.: Bi-objective scheduling for reentrant hybrid flow shop using Pareto genetic algorithm. Comput. Ind. Eng. 61, 529–541 (2011)

    Article  Google Scholar 

  10. 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, 14–22 (2011)

    Article  Google Scholar 

  11. Chou, C.-W., Chien, C.F., Gen, M.: A multiobjective hybrid genetic algorithm for TFT-LCD module assembly scheduling. IEEE Trans. Autom. Sci. Eng. 11(3), 692–705 (2014)

    Article  Google Scholar 

  12. Luo, H., Du, B., Huang, G.Q., Chen, H., Li, X.: Hybrid flow shop scheduling considering machine electricity consumption cost. Int. J. Prod. Econ. 146, 423–429 (2013)

    Article  Google Scholar 

  13. Lemesre, J., Dhaenens, C., Talbi, E.-G.: An exact parallel method for a bi-objective permutation flowshop problem. Eur. J. Oper. Res. 177, 1641–1655 (2007)

    Article  MathSciNet  Google Scholar 

  14. Tang, L.X., Xuan, H., Liu, J.Y.: A new lagrangian relaxation algorithm for hybrid flowshop scheduling to minimize total weighted completion time. Comput. Oper. Res. 33(11), 3344–3359 (2006)

    Article  Google Scholar 

  15. Brah, S.A.: A comparative analysis of due date based job sequencing rules in a flow shop with multiple processors. Prod. Plan. Control. 7(4), 363–373 (1996)

    Article  Google Scholar 

  16. Koulamas, C., Kyparisis, G.J.: A note on performance guarantees for sequencing three-stage flexible flowshops with identical machines to minimize makespan. IIE Trans. 39(5), 559–563 (2007)

    Article  Google Scholar 

  17. Takaku, K., Yura, K.: Online scheduling aiming to satisfy due date for flexible flow shops. JSME Int. J. C-Mech. Sy. 48(1), 21–25 (2005)

    Article  Google Scholar 

  18. Li, J.-Q., Pan, Q.-K., Wang, F.-T.: A hybrid variable neighborhood search for solving the hybrid flow shop scheduling problem. Appl. Soft Comput. 24, 63–77 (2014)

    Article  Google Scholar 

  19. Cui, Z., Gu, X.: An improved discrete artificial bee colony algorithm to minimize the makespan on hybrid flow shop problems. Neurocomputing. 148, 248–259 (2015)

    Article  Google Scholar 

  20. Azadeh, A., Shoja, B.M., Ghanei, S., Sheikhalishahi, M.: A multi-objective optimization problem for multi-state series-parallel systems: a two-stage flow-shop manufacturing system. Reliab. Eng. Syst. Safe. 136, 62–74 (2015)

    Article  Google Scholar 

  21. Li, J.-q., Pan, Q.-k.: Solving the large-scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm. Inf. Sci. (2014) Article in press

    Google Scholar 

  22. Gen, M., Lin, L.: Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey. J. Intell. Manuf. 25, 849–866 (2014)

    Article  Google Scholar 

  23. Yenisey, M.M., Yagmahan, B.: Multi-objective permutation flow shop scheduling problem: literature review, classification and current trends. Omega 45, 119–135 (2014)

    Article  Google Scholar 

  24. Ruiz, R., Vázquez-Rodríguez, J.A.: The hybrid flow shop scheduling problem. Eur. J. Oper. Res. 205, 1–18 (2010)

    Article  MathSciNet  Google Scholar 

  25. Sawik, T.: Scheduling in Supply Chains Using Mixed Integer Programming. Wiley, New Jersey (2011)

    Book  Google Scholar 

  26. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA II. IEEE T. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by Thailand Research Fund (TRF). This support is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Department of Industrial, Faculty of Engineering, King Mongkut’s University of Technology North Bangkok, 1518 Pracharat 1 Road, Bangsue, Bangkok, Thailand

    Wanida Laoraksakiat

  2. Operations Research and Engineering Management Research Center, Department of Industrial Engineering, Faculty of Engineering, King Mongkut’s University of Technology North Bangkok, 1518 Pracharat 1 Road, Bangsue, Bangkok, Thailand

    Krisada Asawarungsaengkul

Authors
  1. Wanida Laoraksakiat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Krisada Asawarungsaengkul
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Krisada Asawarungsaengkul .

Editor information

Editors and Affiliations

  1. School of Information Technology, Deakin University, Geelong, VIC, Australia

    Jemal H. Abawajy

  2. Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Malaysia

    Mohamed Othman

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Rozaida Ghazali

  4. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Mustafa Mat Deris

  5. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Hairulnizam Mahdin

  6. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Laoraksakiat, W., Asawarungsaengkul, K. (2019). A Multiobjective Genetic Algorithm for Hybrid Flow Shop of a Harddisk Drive’s Manufacturer. In: Abawajy, J., Othman, M., Ghazali, R., Deris, M., Mahdin, H., Herawan, T. (eds) Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015) . Lecture Notes in Electrical Engineering, vol 520. Springer, Singapore. https://doi.org/10.1007/978-981-13-1799-6_20

Download citation

Publish with us

Policies and ethics

Search

Navigation