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Neural network application for fuzzy multi-criteria decision making problems

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  • Golmohammadi, Davood

Abstract

In this paper, a fuzzy multi-criteria decision making model is presented based on a feed forward artificial neural network. This model is used to capture and represent the decision makers' preferences. The topology of the neural network model is developed to train the model. The proposed model can use historical data and update the database information for alternatives over time for future decisions. Basically, multi-criteria decision making problems are formulated, and neural network is used to learn the relation among criteria and alternatives and rank the alternatives. We do not use any utility function for the modeling; however, a unique method is proposed for eliciting the information from decision makers. The proposed model is applicable for a wide variety of multi-attribute decision making problems and can be used for future ranking or selection without managers' judgment effort. Simulation of the managers' decisions is demonstrated in detail and the design and implementation of the model are illustrated by a case study.

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  • Golmohammadi, Davood, 2011. "Neural network application for fuzzy multi-criteria decision making problems," International Journal of Production Economics, Elsevier, vol. 131(2), pages 490-504, June.
    • Handle: RePEc:eee:proeco:v:131:y:2011:i:2:p:490-504
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    References listed on IDEAS

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    Cited by:

    1. Golmohammadi, Davood & Radnia, Naeimeh, 2016. "Prediction modeling and pattern recognition for patient readmission," International Journal of Production Economics, Elsevier, vol. 171(P1), pages 151-161.
    2. María Isabel Lamas Galdo & Javier Telmo Miranda & José Manuel Rebollido Lorenzo & Claudio Giovanni Caccia, 2021. "Internal Modifications to Optimize Pollution and Emissions of Internal Combustion Engines through Multiple-Criteria Decision-Making and Artificial Neural Networks," IJERPH, MDPI, vol. 18(23), pages 1-11, December.
    3. Golmohammadi, Davood, 2016. "Predicting hospital admissions to reduce emergency department boarding," International Journal of Production Economics, Elsevier, vol. 182(C), pages 535-544.
    4. Tsung-Yu Chou, 2020. "Using FQFD and FGRA to Enhance the Advertising Effectiveness of Cross-Regional E-Commerce Platforms," Mathematics, MDPI, vol. 8(4), pages 1-22, April.
    5. Lee, Jooh & Kwon, He-Boong, 2017. "Progressive performance modeling for the strategic determinants of market value in the high-tech oriented SMEs," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 91-102.
    6. Golmohammadi, Davood & Zhao, Lingyu & Dreyfus, David, 2023. "Using machine learning techniques to reduce uncertainty for outpatient appointment scheduling practices in outpatient clinics," Omega, Elsevier, vol. 120(C).
    7. Biswas, Sumana & Ali, Ismail & Chakrabortty, Ripon K. & Turan, Hasan Hüseyin & Elsawah, Sondoss & Ryan, Michael J., 2022. "Dynamic modeling for product family evolution combined with artificial neural network based forecasting model: A study of iPhone evolution," Technological Forecasting and Social Change, Elsevier, vol. 178(C).

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