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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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    2. Vladyslav Sotnyk & Artem Kupchyn & Viktor Trynchuk & Vladimer Glonti & Larisa Belinskaja, 2022. "Fuzzy Logic Decision-Making Model for Technology Foresight," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 139-159.
    3. 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.
    4. 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.
    5. 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.

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