Skip to main content
Springer Nature Link
Log in

Cooperative Designing of Machine Layout Using Teaching Learning Based Optimisation and Its Modifications

  • Conference paper
  • First Online:
Cooperative Design, Visualization, and Engineering (CDVE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12341))

Abstract

A variation of customer demand over time periods has resulted in production layout’s efficiency especially in term of material handling cost. Machine re-location approach can help to maintain the flow distances but the costs related to the machine movement may be imposed. Cooperative redesigning of machine layouts between time periods was proposed to minimise both material handling and relocation costs. In this work, Teaching-Learning-Based Optimisation (TLBO) and its modifications were applied to solve non-identical machine layout redesign (MLRD) problem in multi-period multi-row configuration with demand uncertainty scenario. The computational experiments were carried out using eleven benchmarking datasets. The performance of the proposed methods was compared with the conventional Genetic Algorithm, Backtracking Search Algorithm. The effect of relocation cost on the layout design approach was also investigated.

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

Access this chapter

Log in via an institution

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. McKendall, A.R., Shang, J., Kuppusamy, S.: Simulated annealing heuristics for the dynamic facility layout problem. Comput. Oper. Res. 33, 2431–2444 (2006)

    MathSciNet  MATH  Google Scholar 

  2. Tompkins, J.A., White, J.A., Bozer, Y.A., Tanchoco, J.M.A.: Facilities Planning, 4th edn. John Wiley & Sons, Inc, Hoboken (2010)

    Google Scholar 

  3. Kulturel-Konak, S.: Approaches to uncertainties in facility layout problems: Perspectives at the beginning of the 21(st) Century. J. Intell. Manuf. 18, 273–284 (2007)

    Google Scholar 

  4. Chen, G.Y.H., Lo, J.-C.: Dynamic facility layout with multi-objectives. Asia Pac. J. Oper. Res. 31, 1450027 (2014)

    MathSciNet  MATH  Google Scholar 

  5. Ghosh, T., Doloi, B., Dan, P.K.: An Immune Genetic algorithm for inter-cell layout problem in cellular manufacturing system. Prod. Eng. Res. Devel. 10(2), 157–174 (2015). https://doi.org/10.1007/s11740-015-0645-4

    Article  Google Scholar 

  6. Derakhshan Asl, A., Wong, K.Y.: Solving unequal-area static and dynamic facility layout problems using modified particle swarm optimization. J. Intell. Manuf. 28(6), 1317–1336 (2015). https://doi.org/10.1007/s10845-015-1053-5

    Article  Google Scholar 

  7. Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput.-Aid. Des. 43, 303–315 (2011)

    Google Scholar 

  8. Panda, S., Panda, S.N., Nanda, P., Mishra, D.: Comparative study on optimum design of rolling element bearing. Tribol. Int. 92, 595–604 (2015)

    Google Scholar 

  9. Shao, W.S., Pi, D.C., Shao, Z.S.: A hybrid discrete optimization algorithm based on teaching-probabilistic learning mechanism for no-wait flow shop scheduling. Knowl.-Based Syst. 107, 219–234 (2016)

    Google Scholar 

  10. Bhattacharyya, B., Babu, R.: Teaching learning based optimization algorithm for reactive power planning. Int. J. Electr. Power Energ. Syst. 81, 248–253 (2016)

    Google Scholar 

  11. Tang, Li., Wang, Y., Ding, X., Yin, H., Xiong, R., Huang, S.: Topological local-metric framework for mobile robots navigation: a long term perspective. Auton. Rob. 43(1), 197–211 (2018). https://doi.org/10.1007/s10514-018-9724-7

    Article  Google Scholar 

  12. Rao, R.V., Kalyankar, V.D.: Multi-objective multi-parameter optimization of the industrial LBW process using a new optimization algorithm. Proc. Inst. Mech. Eng. Part B-J. Eng. Manuf. 226, 1018–1025 (2012)

    Google Scholar 

  13. Verma, P., Om, H.: A novel approach for text summarization using optimal combination of sentence scoring methods. Sādhanā 44(5), 1–15 (2019). https://doi.org/10.1007/s12046-019-1082-4

    Article  Google Scholar 

  14. Tuo, S.H., He, H.: Solving complex cardinality constrained mean variance portfolio optimization problems using hybrid HS and TLBO algorithm. Econ. Comput. Econ. Cybern. Stud. 52, 231–248 (2018)

    Google Scholar 

  15. Singh, S., Ashok, A., Kumar, M., Rawat, T.K.: Adaptive infinite impulse response system identification using teacher learner based optimization algorithm. Appl. Intell. 49(5), 1785–1802 (2018). https://doi.org/10.1007/s10489-018-1354-4

    Article  Google Scholar 

  16. Pourvaziri, H., Naderi, B.: A hybrid multi-population genetic algorithm for the dynamic facility layout problem. Appl. Soft Comput. 24, 457–469 (2014)

    Google Scholar 

  17. Samarghandi, H., Taabayan, P., Behroozi, M.: Metaheuristics for fuzzy dynamic facility layout problem with unequal area constraints and closeness ratings. Int. J. Adv. Manuf. Tech. 67, 2701–2715 (2013)

    Google Scholar 

  18. Moslemipour, G., Lee, T.S., Loong, Y.T.: Performance Analysis of Intelligent Robust Facility Layout Design. Chin. J. Mech. Eng. 30(2), 407–418 (2017). https://doi.org/10.1007/s10033-017-0073-9

    Article  Google Scholar 

  19. Hosseini, S., Al Khaled, A., Vadlamani, S.: Hybrid imperialist competitive algorithm, variable neighborhood search, and simulated annealing for dynamic facility layout problem. Neural Comput. Appl. 25, 1871–1885 (2014)

    Google Scholar 

  20. Montreuil, B., Laforge, A.: Dynamic layout design given a scenario tree of probable futures. Eur. J. Oper. Res. 63, 271–286 (1992)

    Google Scholar 

  21. Corry, P., Kozan, E.: Ant colony optimisation for machine layout problems. Comput. Optim. Appl. 28, 287–310 (2004)

    MathSciNet  MATH  Google Scholar 

  22. Vitayasak, S., Pongcharoen, P.: Performance improvement of Teaching-Learning-Based Optimisation for robust machine layout design. Expert Syst. Appl. 98, 129–152 (2018)

    Google Scholar 

  23. Vitayasak, S., Pongcharoen, P., Hicks, C.: Robust machine layout design under dynamic environment: Dynamic customer demand and machine maintenance. Expert Syst. Appl. X 3, 100015 (2019)

    Google Scholar 

  24. Vitayasak, S., Pongcharoen, P., Hicks, C.: A tool for solving stochastic dynamic facility layout problems with stochastic demand using either a Genetic Algorithm or modified Backtracking Search Algorithm. Int. J. Prod. Econ. 190, 146–157 (2017)

    Google Scholar 

Download references

Acknowledgement

This work was part of the research project supported by the Thailand Research Fund under the grant number MRG6280168.

Author information

Authors and Affiliations

  1. Centre of Operations Research and Industrial Applications, Department of Industrial Engineering, Faculty of Engineering, Naresuan University, Phitsanulok, 65000, Thailand

    Srisatja Vitayasak & Pupong Pongcharoen

Authors
  1. Srisatja Vitayasak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pupong Pongcharoen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pupong Pongcharoen .

Editor information

Editors and Affiliations

  1. University of the Balearic Islands, Palma, Mallorca, Spain

    Yuhua Luo

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vitayasak, S., Pongcharoen, P. (2020). Cooperative Designing of Machine Layout Using Teaching Learning Based Optimisation and Its Modifications. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2020. Lecture Notes in Computer Science(), vol 12341. Springer, Cham. https://doi.org/10.1007/978-3-030-60816-3_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-60816-3_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60815-6

  • Online ISBN: 978-3-030-60816-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics