A Short Review of EOQ Models and Fuzzy Theory in Inventory Management

Authors

  • Ankit Dubey * VIT-AP University, Inavolu, Beside AP Secretariat, Amaravati AP, India.‎
  • Ranjan Kumar VIT-AP University, Inavolu, Beside AP Secretariat, Amaravati AP, India‎.

https://doi.org/10.48313/scodm.v1i1.18

Abstract

In our manuscript, we investigate diverse approaches and methodologies proposed by researchers and scientists. Our analysis encompasses supply chain management, vendor management, and healthcare systems. Specifically, we delve into Economic Order Quantity (EOQ) within IM, exploring its implications. Additionally, we aim to present literature on fuzzy theory, including discussions about triangular and trapezoidal fuzzy sets. Recognizing that classical theory grapples with uncertainty, we underscore the significance of comprehending fuzzy theory through relevant scholarly works.    

Keywords:

Economic Order Quantity, SCM, IM

References

  1. [1] Sharma, J. K. (2006). Operations research: theory and applications. MACMILAN Publishers.https://www.amirajcollege.in/wp-content/uploads/2020/10/3151910-operations-research-theory-and-applica-

  2. [2] tions-by-j.-k.-sharma-z-lib.org_.pdf

  3. [3] Hussein, H. A., Shiker, M. A. K., & Zabiba, M. S. M. (2020). A new revised efficient of vam to find the

  4. [4] initial solution for the transportation problem. Journal of physics: Conference series (p. 12032). IOP Publishing. https://doi.org/10.1088/1742-6596/1591/1/012032

  5. [5] Singh, A., Wiktorsson, M., & Hauge, J. B. (2021). Trends in machine learning to solve problems in logistics.

  6. [6] Procedia cirp, 103, 67–72. https://doi.org/10.1016/j.procir.2021.10.010

  7. [7] Dubey, A., & Kumar, R. (2023). Extended uncertainty principle for inventory control: an updated review

  8. [8] of environments and applications. International journal of neutrosophic science, 21(4), 8–20. https://doi.org/10.54216/IJNS.210401

  9. [9] Tripathi, S. K., & Kumar, R. (2023). A review of neutrosophic linear programming problems under uncertain environments. International journal of neutrosophic science, 21(4), 94–105. https://doi.org/10.54216/IJNS.210410

  10. [10] Pratyusha, M. N., & Kumar, R. (2023). Critical path method and project evaluation and review technique under uncertainty:a state-of-art review. International journal of neutrosophic science, 21(3), 143– 153. https://doi.org/10.54216/IJNS.210314

  11. [11] Dubey, A., & Kumar, R. (2024). Recent trends and advancements in inventory management. EAI endorsed

  12. [12] transactions on scalable information systems, 11(2), 1–5. https://doi.org/10.4108/eetsis.4543

  13. [13] Tripathi, S. K., & Kumar, R. (2023). A short literature on linear programming problem. EAI endorsed

  14. [14] transactions on energy web, 10(1), 1–5. https://doi.org/10.4108/ew.4516

  15. [15] Tripathi, S. K., Dey, A., Broumi, S., & Kumar, R. (2024). Exploring neutrosophic linear programming in

  16. [16] advanced fuzzy contexts. Neutrosophic sets and systems, 66, 170–184.

  17. [17] https://doi.org/10.5281/zenodo.10939251

  18. [18] Pratyusha, M. N., Dey, A., Broumi, S., & Kumar, R. (2024). Critical Path method and project evaluation

  19. [19] and review technique: a neutrosophic review. Neutrosophic sets and systems, 67, 135–146. https://doi.org/10.5281/zenodo.11123614

  20. [20] Pratyusha, M. N., & Kumar, R. (2024). Enhancing critical path problemin neutrosophic environment

  21. [21] using Python. CMES - computer modeling in engineering and sciences, 140(3), 2957–2976. https://doi.org/10.32604/cmes.2024.051581

  22. [22] Dubey, A., & Kumar, R. (2024). Inventory model with sensitivity analysis under uncertain environment.

  23. [23] Journal of information and optimization sciences, 45(4), 1081–1092. https://doi.org/10.47974/jios-1693

  24. [24] Pratyusha, M. N., & Kumar, R. (2024). Solving neutrosophic critical path problem using Python. Journal of

  25. [25] information and optimization sciences, 45(4), 897–911. https://doi.org/10.47974/JIOS-1614

  26. [26] Tripathi, S., & Kumar, R. (2024). Solving neutrosophic minimal cost flow problem using multi-objective

  27. [27] linear programming problem. Journal of information and optimization sciences, 45, 1093–1104. https://doi.org/10.47974/JIOS-1694

  28. [28] Edalatpanah, S. A. (2018). Neutrosophic perspective on DEA. Journal of applied research on industrial

  29. [29] engineering, 5(4), 339–345. https://doi.org/10.22105/jarie.2019.196020.1100

  30. [30] Edalatpanah, S. A. (2019). A nonlinear approach for neutrosophic linear programming. Journal of applied

  31. [31] research on industrial engineering, 6(4), 367–373. https://doi.org/10.22105/JARIE.2020.217904.1137

  32. [32] Salimi, P. S., & Edalatpanah, S. A. (2020). Supplier selection using fuzzy AHP method and D-numbers.

  33. [33] Journal of fuzzy extension and applications, 1(1), 1–14. https://doi.org/10.22105/jfea.2020.248437.1007

  34. [34] Edalatpanah, S. A. (2023). A paradigm shift in linear programming: an algorithm without artificial

  35. [35] variables. Systemic analytics, 1(1), 1–10. https://doi.org/10.31181/sa1120232

  36. [36] Sallam, K., Mohamed, M., & Wagdy Mohamed, A. (2023). Internet of things (IoT) in supply chain

  37. [37] management: challenges, opportunities, and best practices. Sustainable machine intelligence journal, 2, 1–3. https://doi.org/10.61185/smij.2023.22103

  38. [38] Khalifa, H. A. E. W., Edalatpanah, S. A., & Bozanic, D. (2024). On min-max goal programming approach for solving piecewise quadratic fuzzy multi- objective de novo programming problems. Systemic analytics, 2(1), 36–48. https://doi.org/10.31181/sa21202411

  39. [39] Zahedi, M., Naghdi Khanachah, S., & Zahedi, M. (2024). Providing a structural model lean sustainable supply chain with total quality management approech in the automotive industry. International journal of research in industrial engineering, 13(2), 152–165. https://doi.org/10.22105/riej.2022.342951.1312

  40. [40] Nazabadi, M. R., Najafi, E., & Rasinojehdehi, R. (2024). Integrated decision-making in production, maintenance, repair, and quality planning using an agent-based simulation. Risk assessment and management decisions, 1(1), 12–21. https://doi.org/10.48314/ramd.v1i1.23

  41. [41] Saeedi, S., Mohammadi, M., & Torabi, S. A. (2015). A de novo programming approach for a robust closedloop supply chain network design under uncertainty: An M/M/1 queueing model. International journal of industrial engineering computations, 6(2), 211–228. https://doi.org/10.5267/j.ijiec.2014.11.002

  42. [42] Harris, F. W. (1990). How many parts to make at once. Operations research, 38(6), 947–950.

  43. [43] Rabbani, M., Rezaei, H., Lashgari, M., & Farrokhi-Asl, H. (2018). Vendor managed inventory control

  44. [44] system for deteriorating items using metaheuristic algorithms. Decision science letters, 7(1), 25–38. https://doi.org/10.5267/j.dsl.2017.4.006

  45. [45] Saha, E., & Ray, P. K. (2019). Modelling and analysis of healthcare inventory management systems.

  46. [46] Opsearch, 56(4), 1179–1198. https://doi.org/10.1007/s12597-019-00415-x

  47. [47] Jiang, Y., Shi, C., & Shen, S. (2019). Service level constrained inventory systems. Production and operations

  48. [48] management, 28(9), 2365–2389. https://doi.org/10.1111/poms.13060

  49. [49] Yadav, A. S., Ahlawat, N., Sharma, N., Swami, A., & Navyata. (2020). Healthcare systems of inventory

  50. [50] control for blood bank storage with reliability applications using genetic algorithm. Advances in mathematics: scientific journal, 9(7), 5133–5142. https://doi.org/10.37418/amsj.9.7.80

  51. [51] Abdolazimi, O., Shishebori, D., Goodarzian, F., Ghasemi, P., & Appolloni, A. (2021). Designing a new mathematical model based on ABC analysis for inventory control problem: A real case study. RAIRO -operations research, 55(4), 2309–2335. https://doi.org/10.1051/ro/2021104

  52. [52] Fikri, A., Andika, A., Dava Cahyoga, M. A., & Ratnasari, A. (2020). Implementation of the FIFO Method in the development of inventory applications for agents sinar baru. Journal of information systems and informatics, 2(2), 216–230. https://doi.org/10.33557/journalisi.v2i2.72

  53. [53] Ajay, S. Y., Abid, M., Bansal, S., Tyagi, S. L., & Kumar, T. (2020). Fifo and lifo in green supply chain inventory model of hazardous substance components industry with storage using simulated annealing. Advances in mathematics: scientific journal, 9(7), 5127–5132. https://doi.org/10.37418/amsj.9.7.79

  54. [54] Zhu, S., Jaarsveld, W. van, & Dekker, R. (2020). Spare parts inventory control based on maintenance

  55. [55] planning. Reliability engineering and system safety, 193, 106600. https://doi.org/10.1016/j.ress.2019.106600 [33] Rashid Hashmi, A., Aina Amirah, N., Yusof, Y., & Noor Zaliha, T. (2020). Exploring the dimensions using

  56. [56] exploratory factor analysis of disruptive factors and inventory control. The economics and finance letters, 7(2), 247–254. https://doi.org/10.18488/journal.29.2020.72.247.254

  57. [57] Ahmed, E. R., Alabdullah, T. T. Y., Ardhani, L., & Putri, E. (2021). The Inventory control system’s weaknesses based on the accounting postgraduate students’ perspectives. Jabe (journal of accounting and business education), 5(2), 2528–7281. https://doi.org/10.26675/jabe.v5i2.19312

  58. [58] Hashmi, A. R., Amirah, N. A., Yusof, Y., & Zaliha, T. N. (2021). Mediation of inventory control practices in proficiency and organizational performance: state-funded hospital perspective. Uncertain supply chain management, 9(1), 89–98. https://doi.org/10.5267/j.uscm.2020.11.006

  59. [59] Bhalla, S., Alfnes, E., Hvolby, H. H., & Sgarbossa, F. (2021). Advances in spare parts classification and

  60. [60] forecasting for inventory control: A literature review. IFAC-papersonline, 54(1), 982–987. https://doi.org/10.1016/j.ifacol.2021.08.118

  61. [61] Song, J. S. J., Xue, Z., & Shen, X. (2021). Demand management and inventory control for substitutable

  62. [62] products. SSRN electronic journal, 12, 1–44. https://doi.org/10.2139/ssrn.3866775

  63. [63] Boute, R. N., Gijsbrechts, J., van Jaarsveld, W., & Vanvuchelen, N. (2022). Deep reinforcement learning

  64. [64] for inventory control: A roadmap. European journal of operational research, 298(2), 401–412. https://doi.org/10.1016/j.ejor.2021.07.016

  65. [65] Mittal, S. (2024). Framework for optimized sales and inventory control: a comprehensive approach for intelligent order management application. International journal of computer trends and technology, 72(3), 61– 65. https://doi.org/10.14445/22312803/ijctt-v72i3p109

  66. [66] Mandal, D. B. (2020). An inventory model for time-varying deteriorating items and weibull distributed ameliorating items with cubic demand under salvage value and shortages. International journal for research in applied science and engineering technology, 8(11), 307–315. https://doi.org/10.22214/ijraset.2020.32126

  67. [67] Samal, D., Mishra, M. R., & Kalam, A. (2022). An EOQ model for Inventory System dependent upon on

  68. [68] hand inventory without shortages. Journal of integrated science and technology, 10(3), 193–197.

  69. [69] Çalışkan, C. (2022). Derivation of the optimal solution for the economic production quantity model with

  70. [70] planned shortages without derivatives. Modelling, 3(1), 54–69. https://doi.org/10.3390/modelling3010004 [43] Patriarca, R., Di Gravio, G., Costantino, F., & Tronci, M. (2020). EOQ inventory model for perishable

  71. [71] products under uncertainty. Production engineering, 14(5–6), 601–612. https://doi.org/10.1007/s11740-020-00986-5

  72. [72] Sundararajan, R., Vaithyasubramanian, S., & Nagarajan, A. (2021). Impact of delay in payment, shortage and inflation on an EOQ model with bivariate demand. Journal of management analytics, 8(2), 267–294. https://doi.org/10.1080/23270012.2020.1811165

  73. [73] Ghai, S., Chauhan, A., & Singh, M. P. (2020). Optimization of the EOQ model with reliability affected

  74. [74] demand rate and uncertainty. International journal of management (IJM), 11(7). https://doi.org/10.34218/IJM.11.7.2020.153

  75. [75] Sundararajan, R., Vaithyasubramanian, S., & Rajinikannan, M. (2022). Price determination of a non-

  76. [76] instantaneous deteriorating EOQ model with shortage and inflation under delay in payment.

  77. [77] International journal of systems science: operations and logistics, 9(3), 384–404. https://doi.org/10.1080/23302674.2021.1905908

Published

2024-08-08

How to Cite

Dubey, A. ., & Kumar, R. (2024). A Short Review of EOQ Models and Fuzzy Theory in Inventory Management. Supply Chain and Operations Decision Making, 1(1), 1-6. https://doi.org/10.48313/scodm.v1i1.18

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