Skip to main content
SpringerLink
Log in

Fine-Tuning of Optimisation Parameters in a Firefly Algorithm in Inventory Management

  • Conference paper
  • First Online:
17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022) (SOCO 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 531))

  • 506 Accesses

Abstract

The interest in the problems related to optimal inventory management at a scientific level goes back to the start of the 20th century. The inventory is a necessary feature to control and to forecast the level of future demand. Inventory control techniques are very important components and most organizations can substantially reduce their costs. This paper presents the firefly algorithm (FFA) for modelling the inventory control in a production system. The aim of this research is fine-tuning of parameters in FFA in inventory management in order to minimize production cost according to the price of items and inventory keeping cost. The experimental results demonstrate that it is possible to select values of FFA parameters that significantly reduce production cost.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Jones, E.C.: Supply Chain Engineering and Logistics Handbook - Inventory and Production Control. CRC Press, Boca Raton (2019)

    Book  Google Scholar 

  2. Lewis, C.: Demand Forecasting and Inventory Control: A Computer Aided Learning Approach. Wiley, New York (1998)

    Google Scholar 

  3. Bartmann, D., Beckmann, M.J.: Inventory Control: Models and Methods. Springer, Heidelberg (1992). https://doi.org/10.1007/978-3-642-87146-7

    Book  MATH  Google Scholar 

  4. Simić, D., Ilin, V., Simić, S.D., Simić, S.: Swarm intelligence methods on inventory management. In: Graña, M., et al. (eds.) SOCO’18-CISIS’18-ICEUTE’18 2018. AISC, vol. 771, pp. 426–435. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-94120-2_41

    Chapter  Google Scholar 

  5. Simić, D., Svirčević, V., Corchado, E., Calvo-Rolle, J.L., Simić, S.D., Simić, S.: Modelling material flow using the Milk run and Kanban systems in the automotive industry. Expert. Syst. 38(1), e1254 (2021). https://doi.org/10.1111/exsy.12546

    Article  Google Scholar 

  6. Simić, D., Simić, S.: Hybrid artificial intelligence approaches on vehicle routing problem in logistics distribution. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B. (eds.) HAIS 2012. LNCS (LNAI), vol. 7208, pp. 208–220. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28942-2_19

    Chapter  Google Scholar 

  7. Simić, D., Svirčević, V., Simić, S.: A hybrid evolutionary model for supplier assessment and selection in inbound logistics. J. Appl. Logic 13(2), Part A, 138–147 (2015). https://doi.org/10.1016/j.jal.2014.11.007

  8. Simić, D., Kovačević, I., Svirčević, V., Simić, S.: 50 years of fuzzy set theory and models for supplier assessment and selection: a literature review. J. Appl. Logic 24, Part A, 85–96 (2017). https://doi.org/10.1016/j.jal.2016.11.016

  9. Simić, D., Kovačević, I., Svirčević, V., Simić, S.: Hybrid firefly model in routing heterogeneous fleet of vehicles in logistics distribution. Logic J. IGPL 23(3), 521–532 (2015). https://doi.org/10.1093/jigpal/jzv011

    Article  MathSciNet  Google Scholar 

  10. Samanta, B., Al-Araimi, S.A.: An inventory control model using fuzzy logic. Int. J. Prod. Econ. 73(3), 217–226 (2001). https://doi.org/10.1016/S0925-5273(00)00185-7

    Article  Google Scholar 

  11. Madamidola, O.A., Daramola, O.A., Akintola, K.G.: Web-based intelligent inventory management system. Int. J. Trend Sci. Res. Dev. 1(4), 164–173 (2017). https://doi.org/10.31142/ijtsrd107

  12. Šustrová, T.: A suitable artificial intelligence model for inventory level optimization. Trends Econ. Manage. 25(1), 48–55 (2016). https://doi.org/10.13164/trends.2016.25.48

    Article  Google Scholar 

  13. Zhivitskaya, H., Safronava, T.: Fuzzy model for inventory control under uncertainty. Central Eur. Res. J. 1(2), 10–13 (2015)

    Google Scholar 

  14. Keynes, J.M.: The General Theory of Employment, Interest, and Money. (Reprint edition) Macmillan and Co., London (1949)

    Google Scholar 

  15. Yang, X-S.: Firefly algorithm, Lévy flights and global optimization. In: Bramer, M., Ellis, R., Petridis, M. (eds.) Research and Development in Intelligent Systems XXVI. Springer, London (2010). https://doi.org/10.1007/978-1-84882-983-1_15

  16. Yang, X.-S.: Cuckoo search and firefly algorithm: overview and analysis. In: Yang, X.-S. (ed.) Cuckoo Search and Firefly Algorithm. SCI, vol. 516, pp. 1–26. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02141-6_1

    Chapter  Google Scholar 

  17. Yang, X.-S. (ed.): Cuckoo Search and Firefly Algorithm. SCI, vol. 516. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02141-6

    Book  MATH  Google Scholar 

  18. Yarpiz. Inventory Control using PSO in MATLAB (2022). https://www.mathworks.com/matlabcentral/fileexchange/53142-inventory-control-using-pso-in-matlab. MATLAB Central File Exchange. Accessed 11 June 2022

  19. Saha, S.K., Kar, R., Mandal, D., Ghoshal, S.: Optimal stable IIR low pass filter design using modified Firefly algorithm. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds.) SEMCCO 2013. LNCS, vol. 8297, pp. 98–109. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-03753-0_10

    Chapter  Google Scholar 

  20. Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04944-6_14

    Chapter  Google Scholar 

Download references

Acknowledgment

This research (paper) has been supported by the Ministry of Education, Science and Technological Development through project no. 451-03-68/2022-14/ 200156 “Innovative scientific and artistic research from the FTS (activity) domain”.

Author information

Authors and Affiliations

  1. Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000, Novi Sad, Serbia

    Dragan Simić, Vladimir Ilin & Svetislav D. Simić

  2. Department of Industrial Engineering, University of A Coruña, Avda. 19 de febrero s/n, 15405, Ferrol, A Coruña, Spain

    José Luis Calvo-Rolle

  3. University of Oviedo, Campus de Llamaquique, 33005, Oviedo, Spain

    José R. Villar

  4. Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 1–9, 21000, Novi Sad, Serbia

    Svetlana Simić

Authors
  1. Dragan Simić
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José Luis Calvo-Rolle
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José R. Villar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vladimir Ilin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Svetislav D. Simić
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Svetlana Simić
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dragan Simić .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of León, León, Spain

    Hilde Pérez García

  3. Mechanical Engineering Department, University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martinez-de-Pison

  4. Inteligencia Artificial, University of Oviedo, A Coruña, La Coruña, Spain

    José Ramón Villar Flecha

  5. Data Science and Big Data Lab, Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso Lora

  6. University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  7. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  8. School of engineering, Pablo Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  9. DIGIP, University of Bergamo, Dalmine, Bergamo, Italy

    Giuseppe Psaila

  10. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  11. Department of Computing Science, University of Salamanca, Salamanca, Spain

    Emilio S. Corchado Rodriguez

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

Simić, D., Calvo-Rolle, J.L., Villar, J.R., Ilin, V., Simić, S.D., Simić, S. (2023). Fine-Tuning of Optimisation Parameters in a Firefly Algorithm in Inventory Management. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_63

Download citation

Publish with us

Policies and ethics

Search

Navigation