IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v32y2021i5d10.1007_s10845-020-01616-8.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01616-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10845-020-01616-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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, June.
    3. 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.
    4. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Avelina Alejo-Reyes & Erik Cuevas & Alma Rodríguez & Abraham Mendoza & Elias Olivares-Benitez, 2020. "An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem," Mathematics, MDPI, vol. 8(9), pages 1-24, August.
    2. Sema Akin Bas & Beyza Ahlatcioglu Ozkok, 2020. "A fuzzy approach to multi-objective mixed integer linear programming model for multi-echelon closed-loop supply chain with multi-product multi-time-period," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 30(1), pages 25-46.
    3. Mohamed Salim Amri Sakhri & Mounira Tlili & Ouajdi Korbaa, 2022. "A memetic algorithm for the inventory routing problem," Journal of Heuristics, Springer, vol. 28(3), pages 351-375, June.
    4. Mohammed Alkahtani, 2022. "Supply Chain Management Optimization and Prediction Model Based on Projected Stochastic Gradient," Sustainability, MDPI, vol. 14(6), pages 1-14, March.
    5. Wenjie Wang & Guangdong Tian & Gang Yuan & Duc Truong Pham, 2023. "Energy-time tradeoffs for remanufacturing system scheduling using an invasive weed optimization algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1065-1083, March.
    6. Hong, Jiangtao & Diabat, Ali & Panicker, Vinay V. & Rajagopalan, Sridharan, 2018. "A two-stage supply chain problem with fixed costs: An ant colony optimization approach," International Journal of Production Economics, Elsevier, vol. 204(C), pages 214-226.
    7. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.
    8. Asen ASENOV & Velizara PENCHEVA & Ivan GEORGIEV, 2021. "Planning And Modeling Of The Time For Acceptance And Stay Of Vehicles At The Loading And Discharging Points," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 16(4), pages 23-34, December.
    9. Sina Nayeri & Mahdieh Tavakoli & Mehrab Tanhaeean & Fariborz Jolai, 2022. "A robust fuzzy stochastic model for the responsive-resilient inventory-location problem: comparison of metaheuristic algorithms," Annals of Operations Research, Springer, vol. 315(2), pages 1895-1935, August.
    10. Abdulaziz T. Almaktoom, 2017. "Stochastic Reliability Measurement and Design Optimization of an Inventory Management System," Complexity, Hindawi, vol. 2017, pages 1-9, August.
    11. Sobhani, A. & Wahab, M.I.M. & Jaber, M.Y., 2019. "The effect of working environment aspects on a vendor–buyer inventory model," International Journal of Production Economics, Elsevier, vol. 208(C), pages 171-183.
    12. Sonja Rosenberg & Sandra Huster & Sabri Baazouzi & Simon Glöser-Chahoud & Anwar Al Assadi & Frank Schultmann, 2022. "Field Study and Multimethod Analysis of an EV Battery System Disassembly," Energies, MDPI, vol. 15(15), pages 1-35, July.
    13. Raghav Prasad Parouha & Pooja Verma, 2022. "An innovative hybrid algorithm for bound-unconstrained optimization problems and applications," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1273-1336, June.
    14. A. Ghodratnama & H. R. Arbabi & A. Azaron, 2020. "A bi˗objective hub location-allocation model considering congestion," Operational Research, Springer, vol. 20(4), pages 2427-2466, December.
    15. Dezhi Zhang & Shuxin Yang & Shuangyan Li & Jiajun Fan & Bin Ji, 2020. "Integrated Optimization of the Location–Inventory Problem of Maintenance Component Distribution for High-Speed Railway Operations," Sustainability, MDPI, vol. 12(13), pages 1-25, July.
    16. Zhang, Ting & Su, Yina & Wang, Ningning, 2023. "Product quality improvement under retailer-direct financing: Effects of attitudes toward extreme weather," International Journal of Production Economics, Elsevier, vol. 257(C).
    17. Amir Hossein Nobil & Amir Hosein Afshar Sedigh & Leopoldo Eduardo Cárdenas-Barrón, 2020. "A multiproduct single machine economic production quantity (EPQ) inventory model with discrete delivery order, joint production policy and budget constraints," Annals of Operations Research, Springer, vol. 286(1), pages 265-301, March.
    18. Farheen Naz & Anil Kumar & Abhijit Majumdar & Rohit Agrawal, 2022. "Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research," Operations Management Research, Springer, vol. 15(1), pages 378-398, June.
    19. Anderson Rogério Faia Pinto & Marcelo Seido Nagano, 2020. "Genetic algorithms applied to integration and optimization of billing and picking processes," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 641-659, March.
    20. Zhen Wang & Qianwang Deng & Like Zhang & Xiaoyan Liu, 2023. "Integrated scheduling of production, inventory and imperfect maintenance based on mutual feedback of supplier and demander in distributed environment," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3445-3467, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:32:y:2021:i:5:d:10.1007_s10845-020-01616-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.