IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v5y2024i2d10.1007_s43069-024-00328-6.html
   My bibliography  Save this article

A Comprehensive Study on Economic Production Quantity with Ramp-Type Demand and Constant Deterioration Under Fuzzy Environment

Author

Listed:
  • Kausik Das

    (University of Kalyani)

  • Sahidul Islam

    (University of Kalyani)

Abstract

In this paper, we have discussed a deterministic inventory model with constant demand rate and finite production rate. The demand function has been considered a ramp-type demand which is basically a Heaviside function. For advertisement of product, the marketing cost has also been considered in this model. Due to uncertainty in various cost parameters, we have taken some of them as pentagonal fuzzy number. We defuzzify the total average cost by graded mean integration technique. This research presents a comprehensive investigation into the Economic Production Quantity (EPQ) model in the context of ramp-type demand and constant deterioration, within a fuzzy environment. In traditional inventory management, the EPQ model is widely utilized to determine the optimal production quantity that minimizes total inventory costs. However, real-world scenarios often involve uncertain and imprecise factors, necessitating the incorporation of fuzzy set theory to handle ambiguity. This study addresses this gap by integrating fuzzy logic into the EPQ framework, considering both ramp-type demand patterns and constant deterioration rates. A mathematical model is developed to optimize the production quantity, taking into account fuzzy demand and deterioration rates. The proposed model is then solved using appropriate optimization techniques known as graded mean integration (GMI) method to obtain the total inventory costs. Two numerical examples have been considered to illustrate the results, and the significant features of the results are discussed. Finally, based on these examples, the effects of different parameters on the total average cost and cycle length along with the optimal value have been studied by sensitivity analyses taking one parameter at a time keeping the other parameters as same. The optimum value of total average cost shows us the validity of our EPQ model. The findings highlight the significance of considering fuzziness in inventory decision-making processes, particularly in environments characterized by uncertain demand patterns and deteriorating inventory items. This research contributes to the existing literature by offering insights into the application of fuzzy logic in EPQ models, thereby enhancing decision-making capabilities in inventory management under uncertain conditions.

Suggested Citation

  • Kausik Das & Sahidul Islam, 2024. "A Comprehensive Study on Economic Production Quantity with Ramp-Type Demand and Constant Deterioration Under Fuzzy Environment," SN Operations Research Forum, Springer, vol. 5(2), pages 1-21, June.
    • Handle: RePEc:spr:snopef:v:5:y:2024:i:2:d:10.1007_s43069-024-00328-6
    DOI: 10.1007/s43069-024-00328-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-024-00328-6
    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/s43069-024-00328-6?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Pal, Shilpi & Mahapatra, G.S. & Samanta, G.P., 2014. "An EPQ model of ramp type demand with Weibull deterioration under inflation and finite horizon in crisp and fuzzy environment," International Journal of Production Economics, Elsevier, vol. 156(C), pages 159-166.
    2. Manna, S.K. & Chaudhuri, K.S., 2006. "An EOQ model with ramp type demand rate, time dependent deterioration rate, unit production cost and shortages," European Journal of Operational Research, Elsevier, vol. 171(2), pages 557-566, June.
    3. S.R. Singh & Chaman Singh, 2010. "Supply chain model with stochastic lead time under imprecise partially backlogging and fuzzy ramp-type demand for expiring items," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 8(4), pages 511-522.
    4. Jitendra Kaushik, 2023. "Inventory model for perishable items for ramp type demand with an assumption of preservative technology and Weibull deterioration," International Journal of Procurement Management, Inderscience Enterprises Ltd, vol. 18(2), pages 238-259.
    5. Skouri, K. & Konstantaras, I. & Papachristos, S. & Ganas, I., 2009. "Inventory models with ramp type demand rate, partial backlogging and Weibull deterioration rate," European Journal of Operational Research, Elsevier, vol. 192(1), pages 79-92, 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. Narayan Singh & Bindu Vaish & Shiv Ray Singh, 2014. "A collaborative strategy for a three echelon supply chain with ramp type demand, deterioration and inflation," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 24(3), pages 77-100.
    2. Sudip Adak & G. S. Mahapatra, 2022. "Effect of reliability on multi-item inventory system with shortages and partial backlog incorporating time dependent demand and deterioration," Annals of Operations Research, Springer, vol. 315(2), pages 1551-1571, August.
    3. Hung, Kuo-Chen, 2011. "An inventory model with generalized type demand, deterioration and backorder rates," European Journal of Operational Research, Elsevier, vol. 208(3), pages 239-242, February.
    4. Sarkar, Biswajit & Sarkar, Sumon, 2013. "An improved inventory model with partial backlogging, time varying deterioration and stock-dependent demand," Economic Modelling, Elsevier, vol. 30(C), pages 924-932.
    5. Pal, Shilpi & Mahapatra, G.S. & Samanta, G.P., 2015. "A production inventory model for deteriorating item with ramp type demand allowing inflation and shortages under fuzziness," Economic Modelling, Elsevier, vol. 46(C), pages 334-345.
    6. Dai, Zhuo & Aqlan, Faisal & Gao, Kuo, 2017. "Optimizing multi-echelon inventory with three types of demand in supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 107(C), pages 141-177.
    7. Pahl, Julia & Voß, Stefan, 2014. "Integrating deterioration and lifetime constraints in production and supply chain planning: A survey," European Journal of Operational Research, Elsevier, vol. 238(3), pages 654-674.
    8. Manish Shukla & Sanjay Jharkharia, 2014. "An inventory model for continuously deteriorating agri-fresh produce: an artificial immune system-based solution approach," International Journal of Integrated Supply Management, Inderscience Enterprises Ltd, vol. 9(1/2), pages 110-135.
    9. Lin, Shih-Wei, 2011. "Inventory models with managerial policy independent of demand," European Journal of Operational Research, Elsevier, vol. 211(3), pages 520-524, June.
    10. Saha, Subrata & Sarkar, Biswajit & Sarkar, Mitali, 2023. "Application of improved meta-heuristic algorithms for green preservation technology management to optimize dynamical investments and replenishment strategies," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 209(C), pages 426-450.
    11. Yeu-Shiang Huang & Hau-Wen Lo & Jyh-Wen Ho, 2021. "Effects of component commonality and perishability on inventory control in assemble-to-order systems," Operational Research, Springer, vol. 21(1), pages 205-229, March.
    12. Bakker, Monique & Riezebos, Jan & Teunter, Ruud H., 2012. "Review of inventory systems with deterioration since 2001," European Journal of Operational Research, Elsevier, vol. 221(2), pages 275-284.
    13. Sarkar, Biswajit & Sarkar, Sumon, 2013. "Variable deterioration and demand—An inventory model," Economic Modelling, Elsevier, vol. 31(C), pages 548-556.
    14. K. Skouri & I. Konstantaras & S. Manna & K. Chaudhuri, 2011. "Inventory models with ramp type demand rate, time dependent deterioration rate, unit production cost and shortages," Annals of Operations Research, Springer, vol. 191(1), pages 73-95, November.
    15. Jagadeesan Viswanath & Rajamanickam Thilagavathi & Krishnasamy Karthik & Miroslav Mahdal, 2023. "A Study of a Two Storage Single Product Inventory System with Ramp Type Demand, N-Phase Prepayment and Purchase for Exigency," Mathematics, MDPI, vol. 11(7), pages 1-17, April.
    16. Bikash Koli Dey & Hyesung Seok, 2024. "Intelligent inventory management with autonomation and service strategy," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 307-330, January.
    17. Paulo Afonso & Vishad Vyas & Ana Antunes & Sérgio Silva & Boris P. J. Bret, 2021. "A Stochastic Approach for Product Costing in Manufacturing Processes," Mathematics, MDPI, vol. 9(18), pages 1-23, September.
    18. Kun-Jen Chung & Jui-Jung Liao & Shy-Der Lin & Sheng-Tu Chuang & Hari Mohan Srivastava, 2019. "The Inventory Model for Deteriorating Items under Conditions Involving Cash Discount and Trade Credit," Mathematics, MDPI, vol. 7(7), pages 1-20, July.
    19. Biswajit Sarkar & Sharmila Saren & Leopoldo Cárdenas-Barrón, 2015. "An inventory model with trade-credit policy and variable deterioration for fixed lifetime products," Annals of Operations Research, Springer, vol. 229(1), pages 677-702, June.
    20. Fernández, Arturo J. & Pérez-González, Carlos J. & Aslam, Muhammad & Jun, Chi-Hyuck, 2011. "Design of progressively censored group sampling plans for Weibull distributions: An optimization problem," European Journal of Operational Research, Elsevier, vol. 211(3), pages 525-532, June.

    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:snopef:v:5:y:2024:i:2:d:10.1007_s43069-024-00328-6. 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.