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

Author

Listed:
  • Sasan Harifi

    (Islamic Azad University)

  • Madjid Khalilian

    (Islamic Azad University)

  • Javad Mohammadzadeh

    (Islamic Azad University)

  • Sadoullah Ebrahimnejad

    (Islamic Azad University)

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.

Suggested Citation

  • Sasan Harifi & Madjid Khalilian & Javad Mohammadzadeh & Sadoullah Ebrahimnejad, 2021. "Optimization in solving inventory control problem using nature inspired Emperor Penguins Colony algorithm," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1361-1375, June.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:5:d:10.1007_s10845-020-01616-8
    DOI: 10.1007/s10845-020-01616-8
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    References listed on IDEAS

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    1. Rahdar, Mohammad & Wang, Lizhi & Hu, Guiping, 2018. "A tri-level optimization model for inventory control with uncertain demand and lead time," International Journal of Production Economics, Elsevier, vol. 195(C), pages 96-105.
    2. Hadi Jahangir & Mohammad Mohammadi & Seyed Hamid Reza Pasandideh & Neda Zendehdel Nobari, 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, Springer, vol. 30(6), pages 2327-2353, August.
    3. Asoke Kumar Bhunia & Laxminarayan Sahoo & Ali Akbar Shaikh, 2019. "Advanced Optimization and Operations Research," Springer Optimization and Its Applications, Springer, number 978-981-32-9967-2, December.
    4. Mohammad Alaghebandha & Vahid Hajipour, 2015. "A soft computing-based approach to optimise queuing-inventory control problem," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(6), pages 1113-1130, April.
    5. Asoke Kumar Bhunia & Laxminarayan Sahoo & Ali Akbar Shaikh, 2019. "Inventory Control Theory," Springer Optimization and Its Applications, in: Advanced Optimization and Operations Research, chapter 0, pages 521-579, Springer.
    6. Seyed Mohsen Mousavi & Ardeshir Bahreininejad & S. Nurmaya Musa & Farazila Yusof, 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, Springer, vol. 28(1), pages 191-206, January.
    7. Mostafa Zandieh & Seyed Omid Mohaddesi, 2019. "Portfolio rebalancing under uncertainty using meta-heuristic algorithm," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 36(1), pages 12-39.
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    Cited by:

    1. Juliana Moletta & Gustavo Dambiski Gomes Carvalho & Revenli Fernanda Nascimento & Bertiene Maria Lack Barboza & Luis Mauricio Resende & Joseane Pontes, 2023. "Business networks of women entrepreneurs: an analysis of the expectation and reality of factors that affect trust in a business network," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1021-1036, March.
    2. Adedugba Adebayo & Inegbedion Daniel & Oreagba Oluwakemi, 2024. "Dynamics of Finished Goods Inventory Control Framework: A Deterministic Request in Product Appropriation," SN Operations Research Forum, Springer, vol. 5(2), pages 1-18, June.

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