Skip to main content
Springer Nature Link
Log in

Competitive Learning and Dynamic Genetic Algorithms for Robust Layout Designs Under Uncertainties

  • Conference paper
  • First Online:
Advances in Swarm Intelligence (ICSI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13968))

Included in the following conference series:

  • 570 Accesses

Abstract

Machine layout design is a crucial part of company’s operations and commonly aimed at maximising the effectiveness of resource utilisation within the manufacturing process to meet the uncertain needs of customers. Previous studies on Genetic Algorithm have rarely applied both adaptive and learning mechanisms simultaneously. This paper presents the development of a new competitive learning Genetic Algorithm (CLGA) and dynamic Genetic Algorithm (DGA) for layout design under machine availability and demand uncertainty scenario. The internal logistics of raw materials within the shop floor (minimum flow distance) was considered. The computational programme was carried out using eight benchmarking datasets. The analysis on the computational results indicated that the proposed methods statistically outperformed the conventional Genetic Algorithm in solution quality and convergent speed.

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

References

  1. Talbi, E.G.: METAHEURISTICS from Design to Implementation. Wiley, Hoboken (2009)

    MATH  Google Scholar 

  2. Eiben, A.E., Michalewicz, Z., Schoenauer, M., Smith, J.E.: Parameter control in evolutionary algorithms. In: Lobo, F.G., Lima, C.F., Michalewicz, Z. (eds.) Parameter Setting in Evolutionary Algorithms. Studies in Computational Intelligence, vol. 54, pp. 19–46. Springer, Berlin (2007). https://doi.org/10.1007/978-3-540-69432-8_2

  3. Chew, E.P., Ong, C.J., Lim, K.H.: Variable period adaptive genetic algorithm. Comput. Ind. Eng. 42, 353–360 (2002)

    Article  Google Scholar 

  4. Michalewicz, Z., Fogel, D.V.: How to Solve It: Modern Heuristics. Springer, Cham (2010)

    MATH  Google Scholar 

  5. Bingul, Z.: Adaptive genetic algorithms applied to dynamic multiobjective problems. Appl. Soft Comput. 7, 791–799 (2007)

    Article  Google Scholar 

  6. Matousek, R., Dobrovsky, L., Kudela, J.: How to start a heuristic? Utilizing lower bounds for solving the quadratic assignment problem. Int. J. Ind. Eng. Comput. 13, 151–164 (2022)

    Google Scholar 

  7. Hameed, A.S., Aboobaider, B.M., Mutar, M.L., Choon, N.H.: A new hybrid approach based on discrete differential evolution algorithm to enhancement solutions of quadratic assignment problem. Int. J. Ind. Eng. Comput. 11, 51–72 (2020)

    Google Scholar 

  8. Lashgari, M., Kia, R., Jolai, F.: Robust optimisation to design a dynamic cellular manufacturing system integrating group layout and workers’ assignment. Eur. J. Ind. Eng. 15, 319–351 (2021)

    Article  Google Scholar 

  9. Sooncharoen, S., Vitayasak, S., Pongcharoen, P., Hicks, C.: Development of a modified biogeography-based optimisation tool for solving the unequal-sized machine and multi-row configuration facility layout design problem. ScienceAsia 48, 12–20 (2022)

    Article  Google Scholar 

  10. Nagarajan, L., Mahalingam, S.K., Gurusamy, S., Dharmaraj, V.K.: Solution for bi-objective single row facility layout problem using artificial bee colony algorithm. Eur. J. Ind. Eng. 12, 252–275 (2018)

    Article  Google Scholar 

  11. Dapa, K., Loreungthup, P., Vitayasak, S., Pongcharoen, P.: Bat algorithm, genetic algorithm and shuffled frog leaping algorithm for designing machine layout. In: Ramanna, S., Lingras, P., Sombattheera, C., Krishna, A. (eds.) MIWAI 2013. LNCS (LNAI), vol. 8271, pp. 59–68. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-44949-9_6

    Chapter  Google Scholar 

  12. Hosseini, S.S., Azimi, P., Sharifi, M., Zandieh, M.: A new soft computing algorithm based on cloud theory for dynamic facility layout problem. RAIRO Oper. Res. 55, S2433–S2453 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  13. Guo, W., Jiang, P., Yang, M.: Unequal area facility layout problem-solving: a real case study on an air-conditioner production shop floor. Int. J. Prod. Res. 61, 1479–1496 (2023)

    Article  Google Scholar 

  14. Pourvaziri, H., Salimpour, S., Akhavan Niaki, S.T., Azab, A.: Robust facility layout design for flexible manufacturing: a doe-based heuristic. Int. J. Prod. Res. 60, 5633–5654 (2022)

    Article  Google Scholar 

  15. El-Mihoub, T.A., Hopgood, A.A., Nolle, L., Battersby, A.: Hybrid genetic algorithms: a review. Eng. Lett. 13, 124–137 (2006)

    Google Scholar 

  16. Keller, B., Buscher, U.: Single row layout models. Eur. J. Oper. Res. 245, 629–644 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  17. Wahab, M.I.M., Stoyan, S.J.: A dynamic approach to measure machine and routing flexibilities of manufacturing systems. Int. J. Prod. Econ. 113, 895–913 (2008)

    Article  Google Scholar 

  18. La Scalia, G., Micale, R., Enea, M.: Facility layout problem: bibliometric and benchmarking analysis. Int. J. Ind. Eng. Comput. 10, 453–472 (2019)

    Google Scholar 

  19. Deep, K.: Machine cell formation for dynamic part population considering part operation trade-off and worker assignment using simulated annealing-based genetic algorithm. Eur. J. Ind. Eng. 14, 189–216 (2020)

    Article  Google Scholar 

  20. Alam, M.S., Islam, M.M., Yao, X., Murase, K.: Diversity guided evolutionary programming: a novel approach for continuous optimization. Appl. Soft Comput. 12, 1693–1707 (2012)

    Article  Google Scholar 

  21. Eiben, Á.E., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Trans. Evol. Comput. 3, 124–141 (1999)

    Article  Google Scholar 

  22. Aleti, A., Grunske, L.: Test data generation with a Kalman filter-based adaptive genetic algorithm. J. Syst. Softw. 103, 343–352 (2015)

    Article  Google Scholar 

  23. Senthil Babu, S., Vinayagam, B.K.: Surface roughness prediction model using adaptive particle swarm optimization (APSO) algorithm. J. Intell. Fuzzy Syst. 28, 345–360 (2015)

    Article  Google Scholar 

  24. Balakrishnan, J.D., Cheng, C.H., Conway, D.G., Lau, C.M.: A hybrid genetic algorithm for the dynamic plant layout problem. Int. J. Prod. Econ. 86, 107–120 (2003)

    Article  Google Scholar 

  25. Datta, D., Amaral, A.R.S., Figueira, J.R.: Single row facility layout problem using a permutation-based genetic algorithm. Eur. J. Oper. Res. 213, 388–394 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  26. Peng, Y.F., Zeng, T., Fan, L.Z., Han, Y.J., Xia, B.X.: An improved genetic algorithm based robust approach for stochastic dynamic facility layout problem. Discrete Dyn. Nat. Soc. 2018, 1–8 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  27. Gong, J., Zhang, Z., Liu, J., Guan, C., Liu, S.: Hybrid algorithm of harmony search for dynamic parallel row ordering problem. J. Manuf. Syst. 58, 159–175 (2021)

    Article  Google Scholar 

  28. Zouein, P.P., Kattan, S.: An improved construction approach using ant colony optimization for solving the dynamic facility layout problem. J. Oper. Res. Soc. 73, 1517–1531 (2022)

    Article  Google Scholar 

  29. Khajemahalle, L., Emami, S., Keshteli, R.N.: A hybrid nested partitions and simulated annealing algorithm for dynamic facility layout problem: a robust optimization approach. Infor 59, 74–101 (2021)

    MathSciNet  MATH  Google Scholar 

  30. Salimpour, S., Pourvaziri, H., Azab, A.: Semi-robust layout design for cellular manufacturing in a dynamic environment. Comp. Oper. Res. 133 (2021)

    Google Scholar 

  31. Yang, T., Brett, A.P.: Flexible machine layout design for dynamic and uncertain production environments. Eur. J. Oper. Res. 108, 49–64 (1998)

    Article  MATH  Google Scholar 

  32. Siddique, P.J., Luong, H.T., Shafiq, M.: An optimal joint maintenance and spare parts inventory model. Int. J. Ind. Syst. Eng. 29, 177–192 (2018)

    Google Scholar 

  33. Yeh, R.H., Kao, K.-C., Chang, W.L.: Preventive-maintenance policy for leased products under various maintenance costs. Expert Syst. Appl. 38, 3558–3562 (2011)

    Article  Google Scholar 

  34. Lu, Z.Q., Cui, W.W., Han, X.L.: Integrated production and preventive maintenance scheduling for a single machine with failure uncertainty. Comput. Ind. Eng. 80, 236–244 (2015)

    Article  Google Scholar 

  35. Parika, W., Seesuaysom, W., Vitayasak, S., Pongcharoen, P.: Bat algorithm for designing cell formation with a consideration of routing flexibility. In 2013 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2013, pp. 1353–1357 (2014)

    Google Scholar 

  36. Thioulouse, J., Dray, S.: Interactive multivariate data analysis in R with the ade4 and ade4TkGUI packages. J. Stat. Softw. 22, 1–14 (2007)

    Article  Google Scholar 

  37. 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 

  38. Vitayasak, S., Pongcharoen, P.: Interaction of crossover and mutation operations for designing non-rotatable machine layout. In: Operations Research Network Conference, Thailand (2011)

    Google Scholar 

Download references

Acknowledgements

This work was a part of research projects under grant numbers R2565C006 and R2566B066. The work was also co-funded by the National Research Council of Thailand and Naresuan University under grant number N42A650329.

Author information

Authors and Affiliations

  1. Centre of Operations Research and Industrial Applications (CORIA), 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. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Harbin Institute of Technology, Shenzhen, China

    Wenjian Luo

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vitayasak, S., Pongcharoen, P. (2023). Competitive Learning and Dynamic Genetic Algorithms for Robust Layout Designs Under Uncertainties. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13968. Springer, Cham. https://doi.org/10.1007/978-3-031-36622-2_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-36622-2_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36621-5

  • Online ISBN: 978-3-031-36622-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics