پیوندهای مفید
آمار
| تعداد نشریات | 20 |
| تعداد شماره‌ها | 551 |
| تعداد مقالات | 8,322 |
| تعداد مشاهده مقاله | 39,797,559 |
| تعداد دریافت فایل اصل مقاله | 6,424,225 |
Present a mixed approach of neural network and bat algorithm to predict customer demand in the supply chain to reduce the Bullwhip effect | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 6، دوره 15، شماره 4، تیر 2024، صفحه 65-78 اصل مقاله (1.38 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2023.28672.3956 | ||
| نویسندگان | ||
| Milad Rezaeefard1؛ Nazanin Pilevari* 2، 3؛ Farshad Faezy Razi4 | ||
| 1Department of Industrial Management, College of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 2Department of Industrial Management, Islamic Azad university, West Tehran Branch, Tehran, Iran | ||
| 3Visitor Professor of Science and Research Branch, Islamic Azad university, Tehran, Iran | ||
| 4Department of Industrial Management, Semnan Branch, Islamic Azad University, Semnan, Iran | ||
| تاریخ دریافت: 19 مهر 1401، تاریخ بازنگری: 18 اردیبهشت 1402، تاریخ پذیرش: 21 اردیبهشت 1402 | ||
| چکیده | ||
| Many studies have addressed supply chain that shows the importance of the subject and the competition in the supply chain consisting of several companies. The previous studies address the issue of reducing the Bullwhip Effect in the supply chain, which is possible by predicting the correct amount of customer demand. This paper improves the accuracy of the model prediction and reduces the existing error in the previous models to attain an accurate and very close-to-reality forecast and also to reduce the Bullwhip Effect in the supply chain. Literature shows the absence of research on the presentation of a metaheuristic algorithm consisting of a neural network and bat algorithm to forecast supply chain demand in manufacturing companies; therefore, this article is innovative. On the other hand, no researchers have addressed Bullwhip Effect reduction using mixed metaheuristic algorithms. Therefore, this article improves the previous models, reduces the number of errors in demand forecasting, and reduces the Bullwhip Effect. For this purpose, the scalable gradient algorithm method is used for better network training. The results indicate the optimal performance of neural network training with a comparable gradient and bat algorithm on reducing the Bullwhip Effect. | ||
| کلیدواژه‌ها | ||
| Bullwhip effect؛ Bat algorithm؛ Neural network؛ Demand forecasting؛ Supply chain | ||
| مراجع | ||
|
[1] H. Bousqaoui, S. Achchab, and K. Tikito, Machine learning applications in supply chains: An emphasis on neural network applications, 3rd Int. Conf. Cloud Comput. Technol. Appl. (CloudTech), IEEE, 2017, pp. 1–7. [2] H. Bousqaoui, S. Achchab, and K. Tikito, Machine learning applications in supply chains: Long short-term memory for demand forecasting, Cloud Comput. Big Data: Technol. Appl. Secur.3, Springer, 2019, pp. 301–317. [3] M.H. Fazel Zarandi and F. Shabany Moghadam, Fuzzy knowledge-based token-ordering policies for bullwhip effect management in supply chains, Knowledge Inf. Syst. 50 (2017), 607–631. [4] J. Feizabadi, Machine learning demand forecasting and supply chain performance, Int.J. Logistics Res. Appl. 25 (2022), no. 2, 119–142. [5] E. Fradinata, Z. M. Kesuma, and S. Rusdiana, Support vector regression and adaptive neuro-fuzzy to measure the bullwhip effect in supply chain, Journal of Physics: Conference Series, vol. 1116, IOP Publishing, 2018, 022010. [6] S. Goel, M. Toufeeq, A. Saxena, and S. Gupta, Countering bullwhip effect in supply chain management: A literature review, J. Supply Chain Manag. Syst. 9 (2020), no. 1, 14. [7] S. Gupta and A. Saxena, Operations-based classification of the bullwhip effect, J. Modell. Manag. 17 (2022), no. 1, 134–153. [8] M.T. Harrison Jr, Exploring the bullwhip effect in supply chain management: Identifying constraints and unblocking bottlenecks with data analytics, Ph.D. Thesis, University of Maryland University College, 2020. [9] G. Henrique de Paula Vidal, R. Goyannes Gusm˜ao Caiado, L. Felipe Scavarda, P. Ivson, and J. Arturo Garza-Reyes, Decision support framework for inventory management combining fuzzy multicriteria methods, genetic algorithm, and artificial neural network, Comput. Ind. Engin. 174 (2022), 108777. [10] J. Li, T. Cui, K. Yang, R. Yuan, L. He, and M. Li, Demand forecasting of e-commerce enterprises based on horizontal federated learning from the perspective of sustainable development, Sustainability 13 (2021), no. 23, 13050. [11] H. Lin, J. Lin, and F. Wang, An innovative machine learning model for supply chain management, J. Inn. Knowledge 7 (2022), no. 4, 100276. [12] I. Meidute-Kavaliauskiene, K. Taskın, S. Ghorbani, R. Cincikait˙e, and R. Kacenauskaite, Reviewing the applications of neural networks in supply chain: Exploring research propositions for future directions, Information 13 (2022), no. 5, 261. [13] M. Rezaeefard, N. Pilevari, F. Faezy Razi, and R. Radfar, Reducing the bullwhip effect in supply chain with factors affecting the customer demand forecasting, Int. J. Serv. Oper. Inf. 12 (2022), no. 2, 144–183. [14] S. Rezaei, S. Shokouhyar, and M. Zandieh, A neural network approach for retailer risk assessment in the aftermarket industry, Benchmark.: Int. J. 26 (2019), no. 5, 1631–1647. [15] T. Thuy Hanh Nguyen, A. Bekrar, T.M. Le, and M. Abed, The effect of machine learning demand forecasting on supply chain performance-the case study of coffee in Vietnam, Modell. Comput. Optim. Inf. Syst. Manag. Sci.: Proc. 4th Int. Conf. Modell. Comput. Optim. Inf. Syst. Manag. Sci.-MCO 2021, Springer, 2022, pp. 247–258. [16] A. Yousefi, A. Rahimzadeh, and A. Moradi, A new hybrid prediction reduces the bullwhip effect of demand in a three-level supply chain, J. Modern Proces. Manufact. Prod. 7 (2018), no. 3, 45–58. [17] M. Zhang, L. Shi, X. Zhuo, and Y. Liu, A bullwhip effect weakening approach based on vmd-svm algorithm under the background of intelligent manufacturing, Processes 9 (2021), no. 11, 1957. [18] N. Zougagh, A. Charkaoui, and A. Echchatbi, Prediction models of demand in supply chain, Procedia Comput. Sci. 177 (2020), 462–467. | ||
|
آمار تعداد مشاهده مقاله: 10,245 تعداد دریافت فایل اصل مقاله: 200 |
||