ISSN (Online): 2812-9709
Journal Menu
Archive
Last Edition
Vol.3, No.4, 2024: pp.141-153
Optimization of fuzzy inventory management in industrial processes using deep learning algorithms: a hybrid approach for enhancing demand forecasting and supply chain efficiency
Authors:
K. Kalaiarasi1
, S. Swathi1
1PG & Research Department of Mathematics, Cauvery College for Women (Autonomous), Tiruchirappalli,
Tamil Nadu, India
Received: 23 September 2024
Revised: 15 November 2024
Accepted: 28 November 2024
Published: 31 December 2024
Abstract:
In today’s dynamic business landscape, effective inventory management is
essential for minimizing costs and maximizing profitability. Traditional
models like EOQ and JIT often fall short in handling demand and supply
uncertainties due to their reliance on precise data. This paper introduces a
novel approach that combines fuzzy logic and deep learning to address
these limitations. Fuzzy logic offers a robust framework for decision-
making under uncertainty, while deep learning improves predictive
accuracy by identifying complex patterns in historical data. By
transforming data into fuzzy sets and applying neural networks for
demand forecasting, the proposed model optimizes inventory levels to
reduce costs and prevent stockouts. A mathematical model and
algorithmic implementation demonstrate the approach’s effectiveness and
a numerical example highlights improvements in inventory control,
including reduced holding costs. This study underscores the potential of
integrating AI techniques for adaptive, data-driven inventory management
with broad applications across various industrial processes.
Keywords:
Inventory, Optimization, Fuzzy Model, Triangular Fuzzy number, Development, Industrial processes, Deep Learning
References:
[1] S.S. Ali, H. Barman, R. Kaur, H. Tomaskova, S.K. Roy, Multi-Product Multi Echelon Measurements of Perishable Supply Chain: Fuzzy Non-Linear Programming Approach. Mathematics, 9(17), 2021: 2093. https://doi.org/10.3390/math9172093
[2] M. Akram, I. Ullah, Solution of Z-number- based multi-objective linear programming models with different membership functions. Information Sciences, 659, 2024: 120100. https://doi.org/10.1016/j.ins.2024.120100
[3] V. Bahmani, M.A. Adibi, E. Mehdizadeh, Integration of Two-Stage Assembly Flow Shop Scheduling and Vehicle Routing Using Improved Whale Optimization Algorithm. Journal of Applied Research on Industrial Engineering, 10(1), 2023: 56-83. https://doi.org/10.22105/jarie.2022.329251.1450
[4] C. Cheng, R. Zhu, A. M. Costa, R. Thompson, X. Huang, Multi-period two-echelon location routing problem for disaster waste clean-up. Transportmetrica A: Transport Science, 18(3), 2022: 1053-1083.
https://doi.org/10.1080/23249935.2021.1916644
[5] S. Chand, J. Ward, A Note on Economic Order Quantity Under Conditions of Permissible Delay in Payments. Journal of the Operational Research Society, 38, 1987: 83-84. https://doi.org/10.1057/jors.1987.10
[6] S. Ghosh, K.H. Küfer, S.K. G.-W. Weber, Carbon mechanism on sustainable multi-objective solid transportation problem for waste management in Pythagorean hesitant fuzzy environment. Complex & Intelligent Systems, 8, 2022: 4115-4143. https://doi.org/10.1007/s40747-022-00686-w
[7] A. Ghodousian, B.S. Rad, O. Ghodousian, A non-linear generalization of optimization problems subjected to continuous max-t- norm fuzzy relational inequalities. Soft Computing, 28, 2024: 4025-4036. https://doi.org/10.1007/s00500-023-09376-2
[8] A. Jaber, R. Younes, P. Lafon, J. Khoder, A Review on Multi-Objective Mixed-Integer Non-Linear Optimization Programming Methods. Eng, 5(3), 2024: 1961-1979. https://doi.org/10.3390/eng5030104
[9] D.H. Jenifer, A Methodology for Solving Fuzzy Linear Programming Problem as a Fuzzy Linear Complementarity Problem. Communications on Applied Nonlinear Analysis, 31(2s), 2024:01-08. https://doi.org/10.52783/cana.v31.587
[10] K.-J. Chung, A theorem on the determination of economic order quantity under conditions of permissible delay in payments. Computers & Operations Research, 25(1), 1998: 49-52. https://doi.org/10.1016/S0305-0548(98)80007-5
[11] H.A.E.-W. Khalifa, A. Al-Quran, F. Al-Sharqi, B. Yusoff, K.W.N. Tajer, A.T. Faisal, A.M.A.B. Awad, Utilization of neutrosophic Kuhn-Tucker’s optimality conditions for Solving Pythagorean fuzzy Two-Level Linear Programming Problems. International Journal of Neutrosophic Science, 23(3), 2023: 87-96. https://doi.org/10.54216/IJNS.230308
[12] P. Kaliyaperumal, P. Muralikrishna, An optimization model for fuzzy nonlinear programming with Beale’s conditions using trapezoidal membership functions. Proyecciones (Antofagasta, On line), 43(2), 2024: 425-446.
https://doi.org/10.22199/issn.0717-6279-5468
[13] B. Pal, A. Sarkar, B. Sarkar, Optimal decisions in a dual-channel competitive green supply chain management under promotional effort. Expert Systems with Applications, 211, 2023: 118315. https://doi.org/10.1016/j.eswa.2022.118315
[14] M. Pervin, S.K. Roy, G. Weber, Analysis of inventory control model with shortage under time-dependent demand and time-varying holding cost including stochastic deterioration. Annals of Operations Research, 260, 2018: 437-460.
https://doi.org/10.1007/s10479-016-2355-5
[15] R. Prasad, I. Maiti, S. K. Das, S. Pramanik, T. Mandal, Fuzzy goal programming approach for solving linear fractional programming problems with fuzzy conditions. Journal of Fuzzy Extension and Applications, 5(3), 2024: 330-352.
https://doi.org/10.22105/jfea.2024.430061.1344
[16] R. Chaudhary, M. Mittal, M.K. Jayaswal, A sustainable inventory model for defective items under fuzzy environment. Decision Analytics Journal, 7, 2023: 100207. https://doi.org/10.1016/j.dajour.2023.100207
[17] L.A. San-Jose, J. Sicilia, B. Abdul-Jalbar, Optimal policy for an inventory system with demand dependent on price, time and frequency of advertisement. Computers & Operations Research, 128, 2021: 105169.
https://doi.org/10.1016/j.cor.2020.105169
[18] S. Supian, Subiyanto, T.R. Megantara, A.T. Bon, Ride-Hailing Matching with Uncertain Travel Time: A Novel Interval-Valued Fuzzy Multi-Objective Linear Programming Approach. Mathematics, 12(9), 2024: 1355.
https://doi.org/10.3390/math12091355
[19] M.R. Seikh, S. Dutta, A non-linear mathematical approach for solving matrix games with picture fuzzy payoffs with application to cyberterrorism attacks. Decision Analytics Journal, 10, 2024: 100394.
https://doi.org/10.1016/j.dajour.2023.100394
[20] Shivani, D. Rani, A. Ebrahimnejad, G. Gupta, Multi-objective non-linear programming problem with rough interval parameters: an application in municipal solid waste management. Complex & Intelligent Systems, 10, 2024: 2983-3002. https://doi.org/10.1007/s40747-023-01305-y
[21] X. Yu, Y. Zhou, X. F. Liu, The two-echelon multi-objective location routing problem inspired by realistic waste collection applications: The composable model and a metaheuristic algorithm. Applied Soft Computing, 94, 2020: 106477. https://doi.org/10.1016/j.asoc.2020.106477
[22] Y. Wang, Y. Sun, X. Guan, J. Fan, M. Xu, H. Wang, Two-echelon multi-period location routing problem with shared transportation resource. Knowledge-Based Systems, 226, 2021: 107168. https://doi.org/10.1016/j.knosys.2021.107168
© 2024 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 5
Number 1
March 2026.
How to Cite
K. Kalaiarasi, S. Swathi, Optimization of Fuzzy Inventory Management in Industrial Processes Using Deep Learning Algorithms: A Hybrid Approach for Enhancing Demand Forecasting and Supply Chain Efficiency. Advanced Engineering Letters, 3(4), 2024: 141-153.
https://doi.org/10.46793/adeletters.2024.3.4.1
More Citation Formats
Kalaiarasi, K., & Swathi, S. (2024). Optimization of Fuzzy Inventory Management in Industrial Processes Using Deep Learning Algorithms: A Hybrid Approach for Enhancing Demand Forecasting and Supply Chain Efficiency. Advanced Engineering Letters, 3(4), 141-153.
https://doi.org/10.46793/adeletters.2024.3.4.1
Kalaiarasi, K., & S. Swathi, “Optimization of Fuzzy Inventory Management in Industrial Processes Using Deep Learning Algorithms: A Hybrid Approach for Enhancing Demand Forecasting and Supply Chain Efficiency.“ Advanced Engineering Letters, vol. 3, no. 4, 2024, pp. 141-153.
https://doi.org/10.46793/adeletters.2024.3.4.1
Kalaiarasi, K., and S. Swathi. 2024. “Optimization of Fuzzy Inventory Management in Industrial Processes Using Deep Learning Algorithms: A Hybrid Approach for Enhancing Demand Forecasting and Supply Chain Efficiency.“ Advanced Engineering Letters, 3 (4): 141-153.
https://doi.org/10.46793/adeletters.2024.3.4.1
Kalaiarasi, K. and Swathi, S. (2024). Optimization of Fuzzy Inventory Management in Industrial Processes Using Deep Learning Algorithms: A Hybrid Approach for Enhancing Demand Forecasting and Supply Chain Efficiency. Advanced Engineering Letters, 3(4), pp. 141-153.
doi: 10.46793/adeletters.2024.3.4.1.