IDEAS home Printed from https://ideas.repec.org/a/spr/opsear/v62y2025i2d10.1007_s12597-024-00817-6.html
   My bibliography  Save this article

An integrated mixture of distribution model for environmental cost with fuzzy demand

Author

Listed:
  • K. Annadurai

    (M.V.Muthiah Government Arts College for Women (Affiliated to Mother Teresa Women’s University, Kodaikanal))

  • V. Rajarajeswari

    (M.V.Muthiah Government Arts College for Women (Affiliated to Mother Teresa Women’s University, Kodaikanal))

Abstract

The idea of encouraging collaboration as well as the sharing of information among supply chain participants to boost system efficiency has garnered a lot of attention as the supply chains have grown longer in recent years. In order to attain a higher level of collaboration and automation in the supply chain. The supply chain occasionally invests in lowering ordering costs in order to simplify and speed up transactions through the use of information technology. If the cost of ordering per order can be efficiently decreased, the total necessary expenses per unit time will immediately improve. To accommodate more practical aspects of fundamental inventory systems, we examine a continuous review model of inventory with a controllable lead time for mixtures of lead time demand distributions, subject to the service level constraint. In many inventory models, uncertainty is due to fuzziness, which is a closed possible approach to reality. In this paper, we propose a fuzzy inventory model with demand using a pentagonal fuzzy number in addition to environmental costs, such as carbon emissions during manufacturing and transportation, to obtain the fuzzy total cost. The signed distance method and the Graded mean integration method are used to defuzzi the cost function and an algorithmic procedure is proposed for determining the optimal decision variables. Moreover, a computational algorithm with the help of a computer code using the software MATLAB 7.0 is developed to solve the problem. Finally, numerical examples are provided to illustrate the algorithmic procedure and sensitivity analysis with respect to different associated parameters has been presented, some observations and managerial implications are presented.

Suggested Citation

  • K. Annadurai & V. Rajarajeswari, 2025. "An integrated mixture of distribution model for environmental cost with fuzzy demand," OPSEARCH, Springer;Operational Research Society of India, vol. 62(2), pages 926-958, June.
  • Handle: RePEc:spr:opsear:v:62:y:2025:i:2:d:10.1007_s12597-024-00817-6
    DOI: 10.1007/s12597-024-00817-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12597-024-00817-6
    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/s12597-024-00817-6?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

    for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Mitali Sarkar & Byung Do Chung, 2021. "Effect of Renewable Energy to Reduce Carbon Emissions under a Flexible Production System: A Step Toward Sustainability," Energies, MDPI, vol. 14(1), pages 1-14, January.
    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. Behnamfar, Reza & Sajadi, Seyed Mojtaba & Tootoonchy, Mahshid, 2022. "Developing environmental hedging point policy with variable demand: A machine learning approach," International Journal of Production Economics, Elsevier, vol. 254(C).
    2. Hachen Ali & Fleming Akhtar & Sudipta Guin & Pritam Kumar Pakhira & Ali Akbar Shaikh & Izhar Ahmad, 2025. "Impact of carbon emission and preservation investment on a manufacturing system for deteriorating item with price and greenness dependent demand via equilibrium optimizer algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(5), pages 1813-1829, May.
    3. Shikha Yadav & Sachin Kumar Mangla & Priyamvada Priyamvada & Aman Borkar & Aditi Khanna, 2024. "An energy‐efficient model for PPE waste management in a closed‐loop supply chain," Business Strategy and the Environment, Wiley Blackwell, vol. 33(2), pages 1191-1207, February.
    4. Yue, Xianghua & Peng, Michael Yao-Ping & Anser, Muhammad Khalid & Nassani, Abdelmohsen A. & Haffar, Mohamed & Zaman, Khalid, 2022. "The role of carbon taxes, clean fuels, and renewable energy in promoting sustainable development: How green is nuclear energy?," Renewable Energy, Elsevier, vol. 193(C), pages 167-178.
    5. Majed Alharbi, 2025. "Investigating a Sustainable Inventory System with Controlled Non-Instantaneous Deterioration for Green Products via the Dragonfly Algorithm," Sustainability, MDPI, vol. 17(3), pages 1-23, January.
    6. Falguni Mahato & Chandan Mahato & Gour Chandra Mahata, 2023. "Sustainable optimal production policies for an imperfect production system with trade credit under different carbon emission regulations," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 10073-10099, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:opsear:v:62:y:2025:i:2:d:10.1007_s12597-024-00817-6. 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.