Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Optimization in solving inventory control problem using nature inspired Emperor Penguins Colony algorithm

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

In the present day markets, it is essential for organizations that manage their supply chain efficiency to sustain their market share and improve profitability. Optimized inventory control is an integral part of supply chain management. In inventory control problems, determining the ordering times and the order quantities of products are the two strategic decisions either to minimize total costs or to maximize total profits. This paper presents three models of inventory control problems. These three models are deterministic single-product, deterministic multi-product, and stochastic single-product. Due to the high computational complexity, the presented models are solved using the Emperor Penguins Colony (EPC) algorithm as a metaheuristic algorithm and a soft computing method. EPC is a newly published metaheuristic algorithm, which has not yet been employed to solve the inventory control problem. The results of applying the proposed algorithm on the models are compared with the results obtained by nine state-of-the-art and popular metaheuristic algorithms. To justify the proposed EPC, both cost and runtime criteria are considered. To find significant differences between the results obtained by algorithms, statistical analysis is used. The results show that the proposed algorithm for the presented models of inventory control has better solutions, lower cost, and less CPU consumption than other algorithms.

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

Access this article

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Ahmadi, E., Masel, D. T., Hostetler, S., Maihami, R., & Ghalehkhondabi, I. (2019). A centralized stochastic inventory control model for perishable products considering age-dependent purchase price and lead time. TOP, 28, 231–269.

  • Ahmadi, E., Süer, G. A., & Al-Ogaili, F. (2018). Solving stochastic shortest distance path problem by using genetic algorithms. Procedia Computer Science, 140, 79–86.

    Article  Google Scholar 

  • Alaghebandha, M., & Hajipour, V. (2015). A soft computing-based approach to optimise queuing-inventory control problem. International Journal of Systems Science, 46(6), 1113–1130.

    Article  Google Scholar 

  • Bhunia, A. K., Sahoo, L., & Shaikh, A. A. (2019). Inventory control theory. In Advanced optimization and operations research (pp. 521–579). Springer, Singapore.

  • Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3–18.

    Article  Google Scholar 

  • Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28–39.

    Article  Google Scholar 

  • Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search. Simulation, 76(2), 60–68.

    Article  Google Scholar 

  • Harifi, S., Khalilian, M., Mohammadzadeh, J., & Ebrahimnejad, S. (2019). Emperor Penguins Colony: A new metaheuristic algorithm for optimization. Evolutionary Intelligence, 12(2), 211–226.

    Article  Google Scholar 

  • Harifi, S., Khalilian, M., Mohammadzadeh, J., & Ebrahimnejad, S. (2020). Optimizing a neuro-fuzzy system based on nature inspired Emperor Penguins Colony optimization algorithm. IEEE Transactions on Fuzzy Systems, 28(6), 1110–1124.

    Article  Google Scholar 

  • Jackson, I., Tolujevs, J., & Reggelin, T. (2018). The combination of discrete-event simulation and genetic algorithm for solving the stochastic multi-product inventory optimization problem. Transport and Telecommunication Journal, 19(3), 233–243.

    Article  Google Scholar 

  • Jahangir, H., Mohammadi, M., Pasandideh, S. H. R., & Nobari, N. Z. (2019). Comparing performance of genetic and discrete invasive weed optimization algorithms for solving the inventory routing problem with an incremental delivery. Journal of Intelligent Manufacturing, 30(6), 2327–2353.

    Article  Google Scholar 

  • Kaasgari, M. A., Imani, D. M., & Mahmoodjanloo, M. (2017). Optimizing a vendor managed inventory (VMI) supply chain for perishable products by considering discount: Two calibrated meta-heuristic algorithms. Computers & Industrial Engineering, 103, 227–241.

    Article  Google Scholar 

  • Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.

    Article  Google Scholar 

  • Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942–1948). IEEE.

  • Kumar, P., Herbert, M., & Rao, S. (2017). Population based metaheuristic algorithm approach for analysis of multi-item multi-period procurement lot sizing problem. Advances in Operations Research, 2017, 1–18.

  • Lee, S., Dekker, I. R., & Colak, A. E. (2019). Memetic algorithm for stochastic inventory optimization with seasonal demand. Master Thesis, Erasmus University Rotterdam. https://thesis.eur.nl/pub/45947/384055-Lee.pdf. [Online]

  • Mandal, S., Maity, A. K., Maity, K., Mondal, S., & Maiti, M. (2011). Multi-item multi-period optimal production problem with variable preparation time in fuzzy stochastic environment. Applied Mathematical Modelling, 35(9), 4341–4353.

    Article  Google Scholar 

  • Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological informatics, 1(4), 355–366.

    Article  Google Scholar 

  • Mendenhall, W., Beaver, R. J., & Beaver, B. M. (2012). Introduction to probability and statistics. Boston: Cengage Learning.

    Google Scholar 

  • Mousavi, S. M., Bahreininejad, A., Musa, S. N., & Yusof, F. (2017). A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network. Journal of Intelligent Manufacturing, 28(1), 191–206.

    Article  Google Scholar 

  • Mousavi, S. M., Hajipour, V., Niaki, S. T. A., & Aalikar, N. (2014a). A multi-product multi-period inventory control problem under inflation and discount: A parameter-tuned particle swarm optimization algorithm. The International Journal of Advanced Manufacturing Technology, 70(9–12), 1739–1756.

    Article  Google Scholar 

  • Mousavi, S. M., Hajipour, V., Niaki, S. T. A., & Alikar, N. (2013). Optimizing multi-item multi-period inventory control system with discounted cash flow and inflation: Two calibrated meta-heuristic algorithms. Applied Mathematical Modelling, 37(4), 2241–2256.

    Article  Google Scholar 

  • Mousavi, S. M., Sadeghi, J., Niaki, S. T. A., Alikar, N., Bahreininejad, A., & Metselaar, H. S. C. (2014b). Two parameter-tuned meta-heuristics for a discounted inventory control problem in a fuzzy environment. Information Sciences, 276, 42–62.

    Article  Google Scholar 

  • Olešovský, V. (2016). Advanced methods of mathematical modelling and their use in the inventory management model. Journal of Eastern Europe Research in Business & Economics, 2016, 1–18.

  • Önal, M., & Albey, E. (2020). Economic lot sizing problem with inventory dependent demand. Optimization Letters, 1–20.

  • Pasandideh, S. H. R., Niaki, S. T. A., & Mousavi, S. M. (2013). Two metaheuristics to solve a multi-item multiperiod inventory control problem under storage constraint and discounts. The International Journal of Advanced Manufacturing Technology, 69(5–8), 1671–1684.

    Article  Google Scholar 

  • Rahdar, M., Wang, L., & Hu, G. (2018). A tri-level optimization model for inventory control with uncertain demand and lead time. International Journal of Production Economics, 195, 96–105.

    Article  Google Scholar 

  • Reynolds, R. G. (1994). An introduction to cultural algorithms. In Proceedings of the third annual conference on evolutionary programming (pp. 131–139). River Edge, NJ: World Scientific.

  • Sadeghi, J., Mousavi, S. M., Niaki, S. T. A., & Sadeghi, S. (2013). Optimizing a multi-vendor multi-retailer vendor managed inventory problem: Two tuned meta-heuristic algorithms. Knowledge-Based Systems, 50, 159–170.

    Article  Google Scholar 

  • Saracoglu, I., Topaloglu, S., & Keskinturk, T. (2014). A genetic algorithm approach for multi-product multi-period continuous review inventory models. Expert Systems with Applications, 41(18), 8189–8202.

    Article  Google Scholar 

  • Shenoy, D., & Rosas, R. (2018). Inventory control systems: Design factors. In Problems & solutions in inventory management (pp. 13–32). Springer, Cham.

  • Shenoy, D., & Rosas, R. (2018). Selective inventory control models. In Problems & solutions in inventory management (pp. 211–229). Springer, Cham.

  • Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation, 12(6), 702–713.

    Article  Google Scholar 

  • Sivanandam, S. N., & Deepa, S. N. (2008). Genetic algorithms. In Introduction to genetic algorithms (pp. 15–37). Springer, Berlin, Heidelberg.

  • Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.

    Article  Google Scholar 

  • Taleizadeh, A. A., Niaki, S. T. A., & Aryanezhad, M. B. (2010). Optimising multi-product multi-chance-constraint inventory control system with stochastic period lengths and total discount under fuzzy purchasing price and holding costs. International Journal of Systems Science, 41(10), 1187–1200.

    Article  Google Scholar 

  • Taleizadeh, A. A., Niaki, S. T. A., Aryanezhad, M. B., & Tafti, A. F. (2010). A genetic algorithm to optimize multiproduct multiconstraint inventory control systems with stochastic replenishment intervals and discount. The International Journal of Advanced Manufacturing Technology, 51(1–4), 311–323.

    Article  Google Scholar 

  • Vahdani, B., Soltani, M., Yazdani, M., & Mousavi, S. M. (2017). A three level joint location-inventory problem with correlated demand, shortages and periodic review system: Robust meta-heuristics. Computers & Industrial Engineering, 109, 113–129.

    Article  Google Scholar 

  • Vaziri, S., Zaretalab, A., Esmaeili, M., & Niaki, S. T. A. (2018). An integrated production and procurement design for a multi-period multi-product manufacturing system with machine assignment and warehouse constraint. Applied Soft Computing, 70, 238–262.

    Article  Google Scholar 

  • Yang, W., Chan, F. T., & Kumar, V. (2012). Optimizing replenishment polices using genetic algorithm for single-warehouse multi-retailer system. Expert Systems with Applications, 39(3), 3081–3086.

    Article  Google Scholar 

  • Zandieh, M., & Mohaddesi, S. O. (2019). Portfolio rebalancing under uncertainty using meta-heuristic algorithm. International Journal of Operational Research, 36(1), 12–39.

    Article  Google Scholar 

  • Zhao, W., & Wang, D. (2018). Simulation-based optimization on control strategies of three-echelon inventory in hybrid supply chain with order uncertainty. IEEE Access, 6, 54215–54223.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous referees for their valuable comments and suggestions that improved the presentation of this paper.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran

    Sasan Harifi, Madjid Khalilian & Javad Mohammadzadeh

  2. Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran

    Sadoullah Ebrahimnejad

Authors
  1. Sasan Harifi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Madjid Khalilian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Javad Mohammadzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sadoullah Ebrahimnejad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sasan Harifi.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Harifi, S., Khalilian, M., Mohammadzadeh, J. et al. Optimization in solving inventory control problem using nature inspired Emperor Penguins Colony algorithm. J Intell Manuf 32, 1361–1375 (2021). https://doi.org/10.1007/s10845-020-01616-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-020-01616-8

Keywords

Search

Navigation