Optimization of Inventory with Fuzzy Multi-Objective Approach
محورهای موضوعی : Financial Accounting
Ardeshir Ahmadian
1
,
Fatemeh Sarraf
2
,
Zohreh Hajiha
3
,
Naser Khani
4
1 - Ph.D candidate, Department of Accounting, South Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Accounting, South Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Department of Accounting, South Tehran Branch, Islamic Azad University, Tehran, Iran
4 - Department of Management, Najaf Abad Tehran Branch, Islamic Azad University, Esfahan, Iran
کلید واژه: Sequential Inventory System , Statistical Averaging Methods, Inventory Optimization,
چکیده مقاله :
Resource management is a part of project management and its ultimate goal is to achieve maximum efficiency with the lowest level of inventory. Resource management is built around optimization and increased efficiency. In this paper, inventory optimization is done using fuzzy approach and statistical averaging methods are used to solve fuzzy multi-objective linear programming problems. These methods have been used to form a goal function of fuzzy multi-objective linear programming problems. First, in order to optimize inventory and find the weight of each product in Isfahan Steel Company, a model of fuzzy multi-objective linear programming problem is estimated. The highest weight of products is related to commodity ingots and the lowest weight related to other products. The Fuzzy Multi-Objective Linear Programming Estimation Model has compared the statistical methods with the Chondrasen method. The results show that this method has the capacity to optimize the amount of inventory, reduce storage costs and reduce interest costs due to working capital.
Resource management is a part of project management and its ultimate goal is to achieve maximum efficiency with the lowest level of inventory. Resource management is built around optimization and increased efficiency. In this paper, inventory optimization is done using fuzzy approach and statistical averaging methods are used to solve fuzzy multi-objective linear programming problems. These methods have been used to form a goal function of fuzzy multi-objective linear programming problems. First, in order to optimize inventory and find the weight of each product in Isfahan Steel Company, a model of fuzzy multi-objective linear programming problem is estimated. The highest weight of products is related to commodity ingots and the lowest weight related to other products. The Fuzzy Multi-Objective Linear Programming Estimation Model has compared the statistical methods with the Chondrasen method. The results show that this method has the capacity to optimize the amount of inventory, reduce storage costs and reduce interest costs due to working capital.
[1] Daneshshakib, M., Application of Fuzzy Approach in Inventory Control Ordering System (Economic Order Quantity Model). New researches in mathematics,2021; vol. 7(29): 129–13. [Online]. Available: https://sid.ir/paper/953982/fa
[2] Zimmermann, H., Fuzzy Programming and Linear Programming with Several Objective Functions. Fuzzy Sets and System, 1978; 1: 45-55. doi:10.1016/0165-0114(78)90031-3
[3] Arrow, K., Harris, T., Marschak, J., Optimal inventory policy. The Econometric Society, 1951; 19(3): 250-272. doi:10.2307/1906813
[4] Bellman, R., Glicksberg, I., Gross, O., On the optimal inventory equation. Management Science, 1955; 2(1): 83–104. doi:10.1287/mnsc.2.1.83
[5] Clark, A., Scarf, H., Optimal policies for a multi-echelon inventory problem. Management Science 1960; 6(4): 475–490. doi:10.1287/mnsc.6.4.475
[6] Chao, X., Zhou, S., Optimal policy for a Mult echelon inventory system with batch ordering and fixed replenishment intervals. Operations Research, 2009; 57(2): 377-390. https://www.jstor.org/stable/25614758
[7] Chen, F., Zheng, Y., Evaluating echelon stock (R, nQ) policies in serial production/inventory systems with stochastic demand. Management Science, 1994; 40(10): 1262–1275. doi:10.1287/mnsc.40.10.1262
[8] van Houtum, G., Scheller-Wolf, A., Yi, J., Optimal control of serial inventory systems with fixed replenishment intervals. Operations Research, 2007; 55(4): 674–687. doi:10.1287/opre.1060.0376
[9] Shang, K., Zhou, S., Optimal and heuristic echelon (r, nQ, T) policies in serial inventory systems with fixed costs. Operations Research, 2010; 58(2): 414-427. https://www.jstor.org/stable/40605926
[10] Nahmias, S., Production and Operations Analysis. McGraw-Hill/Irwin, New York, 2009.
[11] Rudi, N., Groennevelt, H., Randall, T., End-of-period vs. continuous accounting of inventory related costs. Home Operations Research, 2009; 57(6): 1360–1366. doi:10.1287/opre.1090.0752
[12] Bellman, R., Zadeh, L., Decision-Making in Fuzzy Environment. Management Science, 1970; 17: 141-164. doi:10.1287/mnsc.17.4.B141
[13] Tanaka, H., Asai, K., Fuzzy Linear Programming Problems with Fuzzy Numbers. Fuzzy Sets and Systems, 1984; 13: 1-10. doi:10.1016/0165-0114(84)90022-8
[14] Ivan, F., Beatrice, M., Q-Learning for Inventory Management: an application case, Procedia Computer Science, 2024; 232: 2431-2439. doi.org/10.1016/j.procs.2024.02.062
[15] Palanivelu, S., Ekambaram, Ch., Optimal inventory system for deteriorated goods with time-varying demand rate function and advertisement cost, Array, 2023; 19: 100307. doi.org/10.1016/j.array.2023.100307
[16] Nicky, D., Onur, A., An intuitive approach to inventory control with optimal stopping, European Journal of Operational Research, 2023; 311: 921-924. doi.org/10.1016/j.ejor.2023.05.035
[17] Rao, U., Properties of the periodic-review (r, t) inventory control policy for stationary, stochastic demand. Manufacturing & Service Operations Management, 2003; 5(1): 37–53. doi:10.1287/msom.5.1.37.12761
[18] Loganathan, C., Lalitha, M., Solving Multi-Objective Mathematical Programming Problems in Fuzzy Approach. Introduction Journal of Mathematics and Its Applications, 2016; 3: 21-25. https://www.researchgate.net/publication/356718421
[19] Zimmermann, H., Fuzzy Sets in Operational Research. EJOR, 1983; 13: 201-216. doi:10.1016/0377-2217(83)90048-6
[20] Behera, S., Nayak, J., Solution of Multi-Objective Mathematical Programming Problems in Fuzzy Approach. International Journal on Computer Science and Engineering, 2021; 3: 3790-3799. https://www.researchgate.net/publication/356718421
[21] Thakre, P., Shelar, D., Thakre, S., Solving Fuzzy Linear Programming Problem as MOLPP. World Congress on Engineering, 2009; 2(5): 978-988. Available online at http:// www.academicjournals.org/JETR
[22] Nahar, S., Alim, M., A New Geometric Average Technique to Solve Multi-Objective Linear Fractional Programming Problem and Comparison with New Arithmetic Average Technique. IOSR Journal of Mathematics, 2017; 13: 39-52. doi:10.9790/5728-1303013952
[23] Nahar, S., Alim, M., A New Statistical Averaging Method to Solve Multi-Objective Linear Programming Problem. International Journal of Science and Research, 2017; 6: 623-629. doi: 10.11648/j.ml.20180403.12
[24] Veeramani, C., Sumathi, M., Fuzzy Mathematical Programming Approach for Solving Fuzzy Linear Fractional Programming Problem. RAIRO-Operations Research, 2013; 48: 109-122. doi:10.1051/ro/2013056
[25] Ebrahimnejad, A., Tavana, M., A Novel Method for Solving Linear Programming Problems with Symmetric Trapezoidal Fuzzy Numbers. Applied Mathematical Modelling, 2014; 38: 4388-4395. doi:10.1016/j.apm.2014.02.024
[26] Das, S., Mandal, T., Edalatpanah, S., A New Approach for Solving Fully Fuzzy Linear Fractional Programming Problems Using the Multi-Objective Linear Programming, RAIRO-Operations Research, 2016; 51: 285-297. doi.10.1051/ro/2016022
[27] Lotfi, F., Allahviranloo, T., Jondabeh, M., Alizadeh, L., Solving a Fully Fuzzy Linear Programming Using Lexicography Method and Fuzzy Approximate Solution. Applied Mathematical Modelling, 2008; 33: 3151-3156. doi:10.1016/j.apm.2008.10.020
[28] Kumar, A., Kaur, J., Singh, P., A New Method for Solving Fully Fuzzy Linear Programming Problems. Applied Mathematical Modelling, 2010; 35: 817-823. doi.10.1016/j.apm.2010.07.037
[29] Nahar, S., Naznin, SH., Asadujjamam, M., Abdul Alim, M., Solving Fuzzy LPP Using Weighted Sum and Comparisons with Ranking Function. International Journal of Scientific & Engineering Research, 2019; 10(11): 480-484.
[30] Sen, C., A New Approach for Multi Objective Rural Development Planning. The Indian Economic Journal, 1983; 30: 91-96. https://www.researchgate.net/publication/284954216
[31] Akter, M., Jahan, M., Kabir, R., Risk Assessment Based on Fuzzy Synthetic Evaluation Method. Science of the Total Environment, 2018; 658: 818-829. doi:10.1016/j.scitotenv.2018.12.204
[32] Nishad, A., Singh, S., Goal Programming for Solving Fractional Programming Problem in Fuzzy Environment. Applied Mathematics, 2015; 6: 2360-2374. doi:10.4236/am.2015.614208
[33] Pitam, S., Kumar, S., Singh, R., Fuzzy Multi-Objective Linear plus Linear Fractional Programming Problem: Approximation and Goal Programming Approach, International Journal of Mathematics and Computers in Simulation, 2011; 5: 395-404.
[34] Nazemi, SH., Shamsedini, R., Presenting a Model for Classification of Material and Inventory Items Using ABC-FUZZY Method, Industrial Management Perspective, 2011; 3: 83-98.
[35] Esmaeilzadeh, M., Olfat, L., Introducing three new models for the importance level of ABC class items with the aim of reducing inventory costs, Quarterly Journal of Supply Chain Management, 2017; 55: 80-100.
[36] Safari, H., Kordlar, A., Jeihouni, A., Bahrami, F., Pricing perishable products with demand dependent on price and time and taking into account twice discount during the sales period, Production and Operations Management, 2022; 30: 25-46. doi:10.22108/jpom.2022.127521.1347
[37] Reyazi, H., Dorodian, M., Afshar Najafi, B., Inventory control of perishable goods based on shelf space and the effect of nemakala changes along with minimum purchase commitment, Industrial Management Journal, 2022; 44: 168-194. doi:10.22059/imj.2022.340649.1007933
[38] Naseri, GH., Namazi, A., Davodpuor, H., Development of an integrated location and facility inventory model with Lagrange Liberation Approach with a Case Study of Fast Consumption Goods Industry, Supply Chain Management, 2021; 71: 79-91
[39] Fazli, M., Faraji Amoogin, S., A review of meta-heuristic methods for solving location allocation financial problems, Advances in Mathematical Finance and Applications, 2023; 8(3): 719-744. doi:10.22034/amfa.2023.1969098.1807
[40] Radenovic, S., Hasani, P., Impact of Financial Characteristics on Future Corporate Risk-Taking Behavior, Advances in Mathematical Finance and Applications, 2019; 5(2): 129-147. doi:10.22034/amfa.2019.562157.1104
[41] Memarpoura, M., Hafezalkotobb, A., Khalilzadeha, M., Saghaeia, A., Soltani, R., Modelling the Effect of Monetary Policies of Central Bank on Macroeconomic Indicators in Iran using System Dynamics and Fuzzy Multi-Criteria Decision-Making Techniques, Advances in Mathematical Finance and Applications, 2022; 9(1): 1-32. doi:10.22034/ amfa.2022.1959354.1752
[42] Bo, L., Tian, H., Stochastic optimal control and piecewise parameterization and optimization method for inventory control system improvement, Chaos, Solitons & Fractals, 2024; 178: 114258. doi.org/10.1016/j.chaos.2023.114258
[43] Yong, W., Weixin, S., Mohammad, Z., Petr, H., Wenting, X., A multi-objective lot sizing procurement model for multi-period cold chain management including supplier and carrier selection, Omega, 2025; 130: 103165. doi.org/10.1016/j.omega.2024.103165
[44] Jianhua, X., Siyuan, M., Shuyi, W., George Q. H., Meta-inventory management decisions: A theoretical model, 2024; 275: 109339. doi.org/10.1016/j.ijpe.2024.109339
[45] Zamanpour,A., Zanjirdar, M., Davodi Nasr,M, Identify and rank the factors affecting stock portfolio optimization with fuzzy network analysis approach, Financial Engineering And Portfolio Manage-ment,2021;12(47): 210-236
