A fuzzy inventory model for perishable products under demand uncertainty and carbon sensitivity

Authors

https://doi.org/10.48313/uda.v2i2.69

Abstract

Perishable inventory management involves keeping track of perishable products in eco-conscious supply chains under uncertainty in demand, cost, and emission parameters. Classical inventory models fail in such scenarios because they rely upon explicit input values and deterministic assumptions. In this paper, we introduce a fuzzy inventory model for perishable products under uncertain demand/demand cost and carbon-sensitive operational constraints. It stores key parameters like demand rate, holding cost, shelf life, and emission rates in triangular fuzzy numbers to reflect the ambiguity inherent in real-world data. The total cost function includes ordering cost, fuzzy holding cost (if the products are perishable) and fuzzy carbon emission penalties associated with storage and transport activities. Fuzzyification is performed by graded mean integration method to obtain actionable inventory decisions. Numerical analysis shows how the model adapts to several real-life constraints and gives a graphic representation of the costs and tradeoffs between cost and quality, preserving product shelf life and protecting the environment. Sensitivity analysis provides a detailed insight into how fuzziness and emission cost affect optimal order quantity. We propose a novel framework for using fuzzy information ambiguity to enable sustainable inventory planning on perishable products.

Keywords:

Fuzzy inventory model, Perishable products, Demand uncertainty, Triangular fuzzy numbers, Graded mean integration, Sustainability

References

  1. [1] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

  2. [2] Wang, J., & Shu, Y. (2005). Fuzzy decision modeling for supply chain management. International journal of production

  3. [3] economics, 150(1), 107–127. https://doi.org/10.1016/j.fss.2004.07.005

  4. [4] Jamkhaneh, E. H., & Taleizadeh, A. A. (2014). A hybrid fuzzy-stochastic inventory model with partial backordering under

  5. [5] inflation and time discounting. Journal of manufacturing systems, 33(4), 668–676.

  6. [6] Nahmias, S. (1982). Perishable inventory theory: A review. Operations research, 30(4), 680–708.

  7. [7] https://doi.org/10.1287/opre.30.4.680

  8. [8] Goyal, S. K., & Giri, B. C. (2001). Recent trends in modeling of deteriorating inventory. European journal of operational

  9. [9] research, 134(1), 1–16. https://doi.org/10.1016/S0377-2217(00)00248-4

  10. [10] Yu, J., Huang, G. H., & Wang, S. Y. (2017). Fuzzy-robust optimization for planning sustainable agricultural development

  11. [11] under uncertainty. Stochastic environmental research and risk assessment, 31(1), 145–160.

  12. [12] Taleizadeh, A. A., Niaki, S. T. A., Aryanezhad, M. B., & Makui, A. (2013). Pricing and lot sizing for perishable items with

  13. [13] partial backlogging and advance payments using fuzzy approach. Mathematical and computer modelling, 57(1–2), 54–68. [8] Sarkar, B., & Moon, I. (2011). Improved inventory model for perishable products considering inflation and time discount-

  14. [14] ing. Journal of manufacturing systems, 30(2), 93–101.

  15. [15] Banu, S. P., & Uthayakumar, R. (2018). Fuzzy inventory model for a multi-item system with limited shelf life under sto-

  16. [16] chastic demand and shortages. Computers & industrial engineering, 115, 275– 287. https://doi.org/10.22541/au.159076896.69508246

  17. [17] Benjaafar, S., Li, Y., & Daskin, M. S. (2013). Carbon footprint and the management of supply chains: Insights from simple models. IEEE transactions on automation science and engineering, 10(1), 99–116. https://doi.org/10.1109/TASE.2012.2203304

  18. [18] Hovelaque, V., & Bironneau, L. (2015). The carbon-constrained EOQ model with carbon emission dependent demand.

  19. [19] International Journal of Production Economics, 164, 285-291. https://doi.org/10.1016/j.ijpe.2014.11.022

  20. [20] Hua, G., Zhang, R., & Wang, Z. (2021). Green inventory management under carbon emission regulations: A review and classi-

  21. [21] fication. Sustainability, 13(2), 507.

  22. [22] Li, Y. (2025). Design of sustainable supply chain networks for industrial production with the consideration of carbon

  23. [23] emission reduction. Sustainable operations and computers, 6, 229-245. https://doi.org/10.1016/j.susoc.2025.07.001

  24. [24] Shamsabadi, E. A., Salehpour, M., Zandifaez, P., & Dias-da-Costa, D. (2023). Data-driven multicollinearity-aware multi-

  25. [25] objective optimisation of green concrete mixes. Journal of Cleaner Production, 390, 136103.

  26. [26] https://doi.org/10.1016/j.jclepro.2023.136103

  27. [27] Pilati, F., Giacomelli, M., & Brunelli, M. (2024). Environmentally sustainable inventory control for perishable products: A

  28. [28] bi-objective reorder-level policy. International Journal of Production Economics, 274, 109309. https://doi.org/10.1016/j.ijpe.2024.109309

  29. [29] Xie, H., Lu, S., & Tang, X. (2023). TSI-based hierarchical clustering method and regular-hypersphere model for product

  30. [30] quality detection. Computers & Industrial Engineering, 177, 109094. https://doi.org/10.1016/j.cie.2023.109094

  31. [31] Alhaj, M. A., Svetinovic, D., & Diabat, A. (2016). RETRACTED: A carbon-sensitive two-echelon-inventory supply chain

  32. [32] model with stochastic demand. https://doi.org/10.1016/j.resconrec.2015.11.011

  33. [33] Sebatjane, M. (2025). Sustainable inventory models under carbon emissions regulations: Taxonomy and literature review.

  34. [34] Computers & Operations Research, 173, 106865. https://doi.org/10.1016/j.cor.2024.106865

  35. [35] Vishwakarma, A. K., Patro, P. K., & Acquaye, A. (2025). Applications of AI to low carbon decision support system for

  36. [36] global supply chains. Cleaner Logistics and Supply Chain, 100261. https://doi.org/10.1016/j.clscn.2025.100261

  37. [37] Maity, S., Chakraborty, A., De, S. K., & Pal, M. (2023). A study of an EOQ model of green items with the effect of carbon

  38. [38] emission under pentagonal intuitionistic dense fuzzy environment. Soft Computing-A Fusion of Foundations, Methodologies

  39. [39] & Applications, 27(20), 15033-15055. https://doi.org/10.1007/s00500-023-08636-5

  40. [40] Cheng, S., Zhang, F., & Chen, X. (2024). Optimal contract design for a supply chain with information asymmetry under

  41. [41] dual environmental responsibility constraints. Expert Systems with Applications, 237, 121466. https://doi.org/10.1016/j.eswa.2023.121466

  42. [42] Salas-Navarro, K., Romero-Montes, J. M., Acevedo-Chedid, J., Ospina-Mateus, H., Florez, W. F., & Cárdenas-Barrón, L.

  43. [43] E. (2023). Vendor managed inventory system considering deteriorating items andprobabilisticdemand for a three-layer supply

  44. [44] chain. Expert Systems with Applications, 218, 119608. https://doi.org/10.1016/j.eswa.2023.119608

Published

2025-06-11

How to Cite

Behera, J. (2025). A fuzzy inventory model for perishable products under demand uncertainty and carbon sensitivity. Uncertainty Discourse and Applications, 2(2), 99-110. https://doi.org/10.48313/uda.v2i2.69

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