IDEAS home Printed from https://ideas.repec.org/a/wly/jjmath/v2025y2025i1n4040646.html

Optimizing Inventory Costs in Manufacturing Systems Using Trapezoidal and Pentagonal Fuzzy Numbers

Author

Listed:
  • Prasantha Bharathi Dhandapani
  • Kalaiarasi Kalaichelvan
  • Soundaria Ramalingam
  • Mohamed Biomy
  • Taha Radwan

Abstract

Every manufacturing industry strives to always provide impeccable goods. Due to machine failures, labor issues, etc., this is practically unachievable in real‐world situations during the manufacturing run time. As a result, things of subpar quality are produced by the equipment systems. The inferior‐quality products are improved at a cost to make them better, and then they are prepared for sale. The nonlinear programming Lagrangian method is used to determine the best solution, which affects the average monthly cost. In the suggested model, the graded mean integration representation method is used to describe defuzzification while trapezoidal and pentagonal fuzzy numbers are used to calculate the optimal cost though there are different types of fuzzy numbers available that are used to test the optimality. The main aim of the paper is to compare the trapezoidal and pentagonal fuzzy numbers to test the optimal total cost. As a result, the trapezoidal fuzzy number gives an accurate result in all cases, while in the pentagonal fuzzy number, there is a slight deviation in the fuzzy case. So when we go with a higher‐order fuzzy number, the accuracy of the optimal total cost changes. Finally, a graphic comparison using MATLAB is carried out for the two fuzzy numbers and the best out of them is found.

Suggested Citation

  • Prasantha Bharathi Dhandapani & Kalaiarasi Kalaichelvan & Soundaria Ramalingam & Mohamed Biomy & Taha Radwan, 2025. "Optimizing Inventory Costs in Manufacturing Systems Using Trapezoidal and Pentagonal Fuzzy Numbers," Journal of Mathematics, John Wiley & Sons, vol. 2025(1).
    • Handle: RePEc:wly:jjmath:v:2025:y:2025:i:1:n:4040646
    DOI: 10.1155/jom/4040646
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/jom/4040646
    Download Restriction: no
    File URL: https://libkey.io/10.1155/jom/4040646?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
    ---><---

    References listed on IDEAS

    as
    1. V. Kuppulakshmi & C. Sugapriya & J. Kavikumar & D. Nagarajan, 2023. "Fuzzy Inventory Model for Imperfect Items with Price Discount and Penalty Maintenance Cost," Mathematical Problems in Engineering, John Wiley & Sons, vol. 2023(1).
    2. Kalaiarasi Kalaichelvan & Soundaria Ramalingam & Prasantha Bharathi Dhandapani & Víctor Leiva & Cecilia Castro, 2024. "Optimizing the Economic Order Quantity Using Fuzzy Theory and Machine Learning Applied to a Pharmaceutical Framework," Mathematics, MDPI, vol. 12(6), pages 1-22, March.
    3. Sanju Kumar & Ravish Kumar Yadav & Aashish Singh, 2023. "Study on fuzzy inventory model for deteriorating items with recurring seasonal demand pattern," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 24(3), pages 408-424.
    4. Amalesh Kumar Manna & Leopoldo Eduardo Cárdenas-Barrón & Jayanta Kumar Dey & Shyamal Kumar Mondal & Ali Akbar Shaikh & Armando Céspedes-Mota & Gerardo Treviño-Garza, 2022. "A Fuzzy Imperfect Production Inventory Model Based on Fuzzy Differential and Fuzzy Integral Method," JRFM, MDPI, vol. 15(6), pages 1-19, May.
    5. Basim S. O. Alsaedi & Osama Abdulaziz Alamri & Mahesh Kumar Jayaswal & Mandeep Mittal, 2023. "A Sustainable Green Supply Chain Model with Carbon Emissions for Defective Items under Learning in a Fuzzy Environment," Mathematics, MDPI, vol. 11(2), pages 1-36, January.
    6. Ranu Singh & Vinod Kumar Mishra, 2024. "Inventory model using Machine Learning for demand forecast with imperfect deteriorating products and partial backlogging under carbon emissions," Annals of Operations Research, Springer, vol. 335(1), pages 361-383, April.
    Full references (including those not matched with items on IDEAS)

    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. Amandeep Sharma & Prateek Kakkar & Susheela Hooda & Mahender Singh Kaswan, 2026. "How Does Gen- AI Reshape Supply Chain, Logistics, and Manufacturing? A Systematic Bibliometric Review," SN Operations Research Forum, Springer, vol. 7(1), pages 1-28, March.
    2. Qin, Yuming & Lan, Hongjie, 2025. "Simulation study of pharmaceutical supply chains based on system dynamics," Finance Research Letters, Elsevier, vol. 72(C).
    3. Basim S. O. Alsaedi, 2024. "A Sustainable Supply Chain Model with a Setup Cost Reduction Policy for Imperfect Items under Learning in a Cloudy Fuzzy Environment," Mathematics, MDPI, vol. 12(10), pages 1-33, May.
    4. Ashish Negi & Ompal Singh & Ajay Singh Yadav & Krishan Kumar Yadav, 2025. "An Inventory Model for Perishable Goods with Demand that Varies Probabilistically with Selling Price Using a Pentagonal Fuzzy Framework," SN Operations Research Forum, Springer, vol. 6(4), pages 1-28, December.
    5. Abhay Bansal & Aastha Panwar & Bhuvan Unhelkar & Mandeep Mittal, 2025. "Optimizing Inventory for Imperfect and Gradually Deteriorating Items Under Multi-Level Trade Credit in a Sustainable Supply Chain," Mathematics, MDPI, vol. 13(5), pages 1-27, February.
    6. Asterios Stroumpoulis & Evangelia Kopanaki, 2025. "Examining the Relationship Between Sustainable Strategies, Digital Transformation and Organizational Context: Evidence from 3PL Companies in Greece," Sustainability, MDPI, vol. 17(19), pages 1-26, October.
    7. Shireen Al-Hourani & Dua Weraikat, 2025. "A Systematic Review of Artificial Intelligence (AI) and Machine Learning (ML) in Pharmaceutical Supply Chain (PSC) Resilience: Current Trends and Future Directions," Sustainability, MDPI, vol. 17(14), pages 1-27, July.
    8. Asterios Stroumpoulis & Evangelia Kopanaki & Panos T. Chountalas, 2024. "Enhancing Sustainable Supply Chain Management through Digital Transformation: A Comparative Case Study Analysis," Sustainability, MDPI, vol. 16(16), pages 1-29, August.
    9. Osama Abdulaziz Alamri, 2023. "A Supply Chain Model with Carbon Emissions and Preservation Technology for Deteriorating Items under Trade Credit Policy and Learning in Fuzzy," Mathematics, MDPI, vol. 11(13), pages 1-58, June.
    10. Majed G. Alharbi, 2022. "Carbon Reduction Technology Based on Imperfect Production System for Deteriorating Items with Warranty Periods and Greenness Dependent Demand," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    11. Priyan, S. & Udayakumar, R., 2025. "Sustainable supply chain system for defective products with uncertain resources and environmental impacts," Innovation and Green Development, Elsevier, vol. 4(1).
    12. Basim S. O. Alsaedi, 2024. "A Sustainable Supply Chain Model with Variable Production Rate and Remanufacturing for Imperfect Production Inventory System under Learning in Fuzzy Environment," Mathematics, MDPI, vol. 12(18), pages 1-49, September.
    13. Lin Chen & Ting Dong & Jin Peng & Dan Ralescu, 2023. "Uncertainty Analysis and Optimization Modeling with Application to Supply Chain Management: A Systematic Review," Mathematics, MDPI, vol. 11(11), pages 1-45, May.
    14. Chunhua Tang & Erbao Cao & Zhanwen Shi, 2023. "Channel Competition and Coordination of a Dual-Channel Supply Chain under Interval Demand," Mathematics, MDPI, vol. 11(12), pages 1-22, June.
    15. Basim S. O. Alsaedi & Marwan H. Ahelali, 2024. "A Sustainable Supply Chain Model with Low Carbon Emissions for Deteriorating Imperfect-Quality Items under Learning Fuzzy Theory," Mathematics, MDPI, vol. 12(8), pages 1-38, April.

    More about this item

    Statistics

    Access and download statistics

    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:wly:jjmath:v:2025:y:2025:i:1:n:4040646. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/1469 .

    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.