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Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FIS + ANN) and FIS with adaptive neuro-fuzzy inference system (FIS + ANFIS) for inventory control

Author

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  • Prasert Aengchuan

    (Thammasat University)

  • Busaba Phruksaphanrat

    (Thammasat University)

Abstract

Conventional inventory models mostly cope with a known demand and adequate supply, but are not realistic for many industries. In this research, the fuzzy inference system (FIS) model, FIS with artificial neural network (ANN) model and FIS with adaptive neuro-fuzzy inference system (ANFIS) model in which both supply and demand are uncertain were applied for the inventory system. For FIS model, the generated fuzzy rules were applied to draw out the fuzzy order quantity continuously. The order quantity was adjusted according to the FIS model with the evaluation algorithm for the inventory model. The output of FIS model was also used as data for FIS + ANN and FIS + ANFIS models. The FIS + ANFIS model was studied with three membership functions; trapezoidal and triangular (Trap), Gaussian and bell shape. Inventory costs of the proposed models were compared with the stochastic economic order quantity (EOQ) models based on previous data of a case study factory. The results showed that the FIS + ANFIS_Gauss model gave the best performance of total inventory cost saving by more than 75 % compared to stochastic EOQ model.

Suggested Citation

  • Prasert Aengchuan & Busaba Phruksaphanrat, 2018. "Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FIS + ANN) and FIS with adaptive neuro-fuzzy inference system (FIS + ANFIS) for inventory control," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 905-923, April.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:4:d:10.1007_s10845-015-1146-1
    DOI: 10.1007/s10845-015-1146-1
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    1. Andriolo, Alessandro & Battini, Daria & Grubbström, Robert W. & Persona, Alessandro & Sgarbossa, Fabio, 2014. "A century of evolution from Harris׳s basic lot size model: Survey and research agenda," International Journal of Production Economics, Elsevier, vol. 155(C), pages 16-38.
    2. Grubbström, Robert W., 2014. "Dynamic lotsizing with a finite production rate," International Journal of Production Economics, Elsevier, vol. 149(C), pages 68-79.
    3. Khan, M. & Jaber, M.Y. & Wahab, M.I.M., 2010. "Economic order quantity model for items with imperfect quality with learning in inspection," International Journal of Production Economics, Elsevier, vol. 124(1), pages 87-96, March.
    4. Handfield, Robert & Warsing, Don & Wu, Xinmin, 2009. "(Q,r) Inventory policies in a fuzzy uncertain supply chain environment," European Journal of Operational Research, Elsevier, vol. 197(2), pages 609-619, September.
    5. Widyadana, Gede Agus & Wee, Hui Ming, 2012. "An economic production quantity model for deteriorating items with multiple production setups and rework," International Journal of Production Economics, Elsevier, vol. 138(1), pages 62-67.
    6. Glock, C. H. & Grosse, E. H. & Ries, J. M., 2014. "The Lot Sizing Problem: A Tertiary Study," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 63361, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    7. Yongpei Guan, 2011. "Stochastic lot-sizing with backlogging: computational complexity analysis," Journal of Global Optimization, Springer, vol. 49(4), pages 651-678, April.
    8. Glock, Christoph H. & Grosse, Eric H. & Ries, Jörg M., 2014. "The lot sizing problem: A tertiary study," International Journal of Production Economics, Elsevier, vol. 155(C), pages 39-51.
    9. Harvey M. Wagner & Thomson M. Whitin, 1958. "Dynamic Version of the Economic Lot Size Model," Management Science, INFORMS, vol. 5(1), pages 89-96, October.
    10. Azadegan, Arash & Porobic, Lejla & Ghazinoory, Sepehr & Samouei, Parvaneh & Saman Kheirkhah, Amir, 2011. "Fuzzy logic in manufacturing: A review of literature and a specialized application," International Journal of Production Economics, Elsevier, vol. 132(2), pages 258-270, August.
    11. Parlar, Mahmut & Perry, David, 1995. "Analysis of a (Q, r, T) inventory policy with deterministic and random yields when future supply is uncertain," European Journal of Operational Research, Elsevier, vol. 84(2), pages 431-443, July.
    12. Editors, 2014. "International Journal of Systems Science," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 1-1, December.
    13. Samanta, B. & Al-Araimi, S. A., 2001. "An inventory control model using fuzzy logic," International Journal of Production Economics, Elsevier, vol. 73(3), pages 217-226, October.
    14. Yang, L. & Entchev, E., 2014. "Performance prediction of a hybrid microgeneration system using Adaptive Neuro-Fuzzy Inference System (ANFIS) technique," Applied Energy, Elsevier, vol. 134(C), pages 197-203.
    15. Evan L. Porteus, 1986. "Optimal Lot Sizing, Process Quality Improvement and Setup Cost Reduction," Operations Research, INFORMS, vol. 34(1), pages 137-144, February.
    16. Qi Feng, 2010. "Integrating Dynamic Pricing and Replenishment Decisions Under Supply Capacity Uncertainty," Management Science, INFORMS, vol. 56(12), pages 2154-2172, December.
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    2. Abdelhady Ramadan & Salah Kamel & I. Hamdan & Ahmed M. Agwa, 2022. "A Novel Intelligent ANFIS for the Dynamic Model of Photovoltaic Systems," Mathematics, MDPI, vol. 10(8), pages 1-14, April.
    3. Lechtenberg, Sandra & Hellingrath, Bernd, 2021. "Applications of artificial intelligence in supply chain management: Identification of main research fields and greatest industry interests," ERCIS Working Papers 37, University of Münster, European Research Center for Information Systems (ERCIS).
    4. Germán González Rodríguez & Jose M. Gonzalez-Cava & Juan Albino Méndez Pérez, 2020. "An intelligent decision support system for production planning based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1257-1273, June.
    5. Adnan Aktepe & Emre Yanık & Süleyman Ersöz, 2021. "Demand forecasting application with regression and artificial intelligence methods in a construction machinery company," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1587-1604, August.

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