ANFIS model for determining the economic order quantity

Siniša Sremac; Ilija Tanackov; Miloš Kopić; Dunja Radović

doi:10.31181/dmame1802079s

Authors

Siniša Sremac Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
Ilija Tanackov Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
Miloš Kopić Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
Dunja Radović University of East Sarajevo, Faculty of Transport and Traffic Engineering, Doboj, Bosnia and Herzegovina

DOI:

https://doi.org/10.31181/dmame1802079s

Keywords:

Adaptive Neuro-fuzzy Inference Systems, Economic Order Quantity, Supply Chain Management, Logistics Processes

Abstract

The determination of the economic order quantity is important for the rational realization of the logistics process of transport, manipulation and storage in the supply chain. In this paper an expert model for the determination of the economic order quantity has been developed by means of a hybrid method of the artificial intelligence Adaptive neuro-fuzzy inference systems - ANFIS. It has been used for modeling a complex logistics process in which it is difficult to determine interdependence of the presented variables applying classical methods. The hybrid method is applied in order to take advantages of the individual methods of artificial intelligence: fuzzy logic and neural networks. Experts’ experience and information about the company operations for a certain group of items are used to form the model. An analysis of the validity of the model results was performed on the basis of the average relative error. It is shown that the model imitates the work of the expert in the observed company with great accuracy. A sensitivity analysis is also applied which indicates that the model gives valid results. The proposed model is flexible and can be applied to various types of goods in the supply chain management.

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References

Abdel-Aleem, A., El-Sharief, A. M., Hassan A. M., & G. El-Sebaie, G. M. (2017). Implementation of Fuzzy and Adaptive Neuro-Fuzzy Inference Systems in Optimization of Production Inventory Problem. Applied Mathematics & Information Sciences, 11, 289–298.

Abraham, A. (2001). Neuro fuzzy systems: State-of-the-art modeling techniques. In Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13–15, 2001 Proceedings, Part 1 6 (pp. 269-276). Springer Berlin Heidelberg.

Ambukege, G., Justo, G., & Mushi, J. (2017). Neuro fuzzy modelling for prediction of consumer price index. International Journal of Artificial Intelligence and Applications, 8, 33–44.

Azizi, A., Bin Ali, A. Y., & Ping, L. W. (2015). Modelling production uncertainties using the adaptive neuro-fuzzy inference system. South African Journal of Industrial Engineering, 26(1), 224-234.

Bowersox, D. J., Closs, D. J., & Cooper M. B. (2010). Supply Chain Logistics Management. New York: McGraw-Hill.

Cetisli, B. (2010). Development of an adaptive neuro-fuzzy classifier using linguistic hedges: Part 1. Expert Systems with Applications, 37(8), 6093-6101.

Chatfield, D. C., Hayya, J. C., & Cook, D. P. (2013). Stockout propagation and amplification in supply chain inventory systems. International Journal of Production Research, 51(5), 1491-1507

Davis-Sramek, B., & Fugate, B. (2007). State of logistics: a visionary perspective. Journal of Business Logistics, 28(2), 1–34.

Efendigil, T. (2014). Modelling product returns in a closed-loop supply chain under uncertainties: a neuro fuzzy approach. Journal of multiple-valued logic and soft computing, 23, 407–426.

Giannoccaro, I., Pontrandolfo, P., & Scozzi, B. (2003). A fuzzy echelon approach for inventory management in supply chains. European Journal of Operational Research, 149(1), 185–196.

Griffis, S. E., Bell, J. E., & Closs D. J. (2012). Metaheuristics in Logistics and Supply Chain Management. Journal of Business Logistics, 33(2), 90-106.

Guneri, A. F., Ertay, T., & Yucel, A. (2011). An approach based on ANFIS input selection and modeling for supplier selection problem. Expert Systems with Applications, 38(2), 14907–14917.

Gupta, A., & Maranas, C. D. (2003). Managing demand uncertainty in supply chain planning. Computers & chemical engineering, 27(8-9), 1219-1227.

Harris, F. W. (1913). How many parts to make at once. Factory, The Magazine of Management, 10(2), 135–136.

Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.

Keshavarz Ghorabaee, M., Zavadskas, E. K., Amiri, M., & Antucheviciene, J. (2016). A new method of assessment based on fuzzy ranking and aggregated weights (AFRAW) for MCDM problems under type-2 fuzzy environment. Economic computation and economic cybernetics studies and research, 50, 39–68.

Lagodimos, A. G., Skouri, K., Christou, I. T., & Chountalas, P. T. (2018). The discrete-time EOQ model: Solution and implications. European Journal of Operational Research, 266(1), 112-121.

Latif, H. H., Paul, S. K., & Azeem, A. (2014). Ordering policy in a supply chain with adaptive neuro-fuzzy inference system demand forecasting. International Journal of Management Science and Engineering Management, 9(2), 114-124.

Maddah, B., & Noueihed, N. (2017). EOQ holds under stochastic demand, a technical note. Applied Mathematical Modelling, 45, 205-208.

Mardani, A., Zavadskas, E. K., Khalifah, Z., Zakuan, N., Jusoh, A., Nor, K. M., & Khoshnoudi, M. (2017). A review of multi-criteria decision-making applications to solve energy management problems: Two decades from 1995 to 2015. Renewable and Sustainable Energy Reviews, 71, 216-256.

Negnevitsky, M. (2005). Artificial Intelligence: A Guide to Intelligent Systems. 2nd Edition, Edinburgh Gate, England.

Özkan, G., & İnal, M. (2014). Comparison of neural network application for fuzzy and ANFIS approaches for multi-criteria decision making problems. Applied Soft Computing, 24, 232-238.

Pamučar, D., & Ćirović, G. (2018). Vehicle route selection with an adaptive neuro fuzzy inference system in uncertainty conditions, Decision Making: Applications in Management and Engineering, 1, 13-37.

Pamučar, D., Petrović, I., & Ćirović, G. (2018). Modification of the Best-Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers. Expert systems with applications, 91, 89-106.

Paul, S. K., Azeem, A., & Ghosh, A. K. (2015). Application of adaptive neuro-fuzzy inference system and artificial neural network in inventory level forecasting. International Journal of Business Information Systems, 18(3), 268–284.

Rajab, S., & Sharma, V. (2017). A review on the applications of neuro-fuzzy systems in business. Artificial Intelligence Review, Springer, 1–30.

Sremac, S. (2013). Goods flow management model for transport-storage processes, PhD dissertation, Faculty of Technical Sciences, University of Novi Sad.

Stević, Ž., Pamučar, D., Vasiljević, M., Stojić, G., & Korica, S. (2017). Novel integrated multi-criteria model for supplier selection: Case study construction company. Symmetry, 9(11), 279.

Tahmasebi, P., & Hezarkhani, A. (2010). Application of adaptive neuro-fuzzy inference system for grade estimation; case study, Sarcheshmeh porphyry copper deposit, Kerman, Iran. Australian Journal of Basic and Applied Sciences, 4(3), 408-420.

Taleizadeh, A. A., Khanbaglo, M. P. S., & Cárdenas-Barrón, L. E. (2016). An EOQ inventory model with partial backordering and reparation of imperfect products. International Journal of Production Economics, 182, 418-434.

Teksan, Z. M., & Geunes, J. (2016). An EOQ model with price-dependent supply and demand. International Journal of Production Economics, 178, 22-33.

Vahdani, B., Iranmanesh, S. H., Meysam Mousavi, S., & Abdollahzade M. (2012). A locally linear neuro-fuzzy model for supplier selection in cosmetics industry, Applied Mathematical Modelling, 36(10), 4714-4727.

Wallin, C., Rungtusanatham, M. J., & Rabinovich, E. (2006). What is the right inventory management approach for a orderd item. International Journal of Operations & Production Management, 26(1), 50–68.

Yazdani-Chamzini, A., Yakhchali, S. H., Volungevičienė, D., & Zavadskas, E.K. (2012). Forecasting gold price changes by using adaptive network fuzzy inference system. Journal of Business Economics and Management, 13, 994–1100.

Yazdani-Chamzini, A., Zavadskas, E.K., Antucheviciene, Y., & Bausys, R. (2017). A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks. Symmetry, 9(12), 298–311.

Zavadskas, E. K., Govindan, K., Antucheviciene, J., & Turskis, Z. (2016). Hybrid multiple criteria decision-making methods: A review of applications for sustainability issues. Economic research-Ekonomska istraživanja, 29(1), 857-887.