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Feedforward Artificial Neural Networks for Solving Discrete Multiple Criteria Decision Making Problems

Author

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  • Behnam Malakooti

    (Department of Systems, Control and Industrial Engineering, Center for Automation and Intelligent Systems Research, Case Western Reserve University, Cleveland, Ohio 44106)

  • Ying Q. Zhou

    (Trikon Design, Inc., 2295 Opdyke, Auburn Hills, Michigan 48326)

Abstract

Decision making involves choosing some course of action among various alternatives. In almost all decision making problems, there are several criteria for judging possible alternatives. The main concern of the Decision Maker (DM) is to fulfill his conflicting goals while satisfying the constraints of the system. In this paper, we present an Adaptive Feedforward Artificial Neural Network (AF-ANN) approach to solve discrete Multiple Criteria Decision Making (MCDM) problems. The AF-ANN is used to capture and represent the DM's preferences and then to select the most desirable alternative. The AF-ANN can adjust and improve its representation as more information from the DM becomes available. We begin with the assumption that an AF-ANN topology is given, i.e., specific numbers of nodes and links are predetermined. To adjust the parameters of the AF-ANN, we present an iterative learning algorithm consisting of two steps. (a) generating a direction, and (b) a one-dimensional search along that direction. We then present a methodology to obtain the most appropriate AF-ANN topology and set its parameters. The procedure starts with a small number of nodes and links and then adaptively increases the number of nodes and links until the proper topology is obtained. Furthermore, when the set of training patterns (alternatives with their associated evaluations by the DM) changes, the AF-ANN model can adapt itself by re-training or expanding the existing model. Some illustrative examples are presented. To solve discrete MCDM problems by an AF-ANN, we show how to incorporate basic properties of efficiency, concavity, and convexity into the AF-ANN. We formulate the MCDM problems and use the AF-ANN to rank the set of discrete alternatives where each alternative is associated with a set of conflicting and noncommensurate criteria. We present a method for solving discrete MCDM problems through AF-ANNs which consists of. (a) formulating and assessing the utility function by eliciting information from the DM and then training the AF-ANN, and (b) ranking a rating alternatives by using the trained AF-ANN model. Some computational experiments are presented to show the effectiveness of the method.

Suggested Citation

  • Behnam Malakooti & Ying Q. Zhou, 1994. "Feedforward Artificial Neural Networks for Solving Discrete Multiple Criteria Decision Making Problems," Management Science, INFORMS, vol. 40(11), pages 1542-1561, November.
    • Handle: RePEc:inm:ormnsc:v:40:y:1994:i:11:p:1542-1561
    DOI: 10.1287/mnsc.40.11.1542
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    Citations

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    Cited by:

    1. James R. Coakley & Carol E. Brown, 2000. "Artificial neural networks in accounting and finance: modeling issues," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(2), pages 119-144, June.
    2. Behnam Malakooti & Mohamed Komaki & Camelia Al-Najjar, 2021. "Basic Geometric Dispersion Theory of Decision Making Under Risk: Asymmetric Risk Relativity, New Predictions of Empirical Behaviors, and Risk Triad," Decision Analysis, INFORMS, vol. 18(1), pages 41-77, March.
    3. Martyn, Krzysztof & Kadziński, Miłosz, 2023. "Deep preference learning for multiple criteria decision analysis," European Journal of Operational Research, Elsevier, vol. 305(2), pages 781-805.
    4. Doumpos, Michael & Zopounidis, Constantin, 2004. "Developing sorting models using preference disaggregation analysis: An experimental investigation," European Journal of Operational Research, Elsevier, vol. 154(3), pages 585-598, May.
    5. Osterrieder, Philipp & Budde, Lukas & Friedli, Thomas, 2020. "The smart factory as a key construct of industry 4.0: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 221(C).
    6. C Gagné & M Gravel & W L Price, 2005. "Using metaheuristic compromise programming for the solution of multiple-objective scheduling problems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(6), pages 687-698, June.
    7. 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.
    8. Gal, Tomas & Hanne, Thomas, 2006. "Nonessential objectives within network approaches for MCDM," European Journal of Operational Research, Elsevier, vol. 168(2), pages 584-592, January.
    9. Qi, Min & Yang, Sha, 2003. "Forecasting consumer credit card adoption: what can we learn about the utility function?," International Journal of Forecasting, Elsevier, vol. 19(1), pages 71-85.
    10. Lidan Pei & Feifei Jin & Zhiwei Ni & Huayou Chen & Zhifu Tao, 2017. "An automatic iterative decision-making method for intuitionistic fuzzy linguistic preference relations," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(13), pages 2779-2793, October.
    11. Zopounidis, Constantin & Doumpos, Michael, 2002. "Multicriteria classification and sorting methods: A literature review," European Journal of Operational Research, Elsevier, vol. 138(2), pages 229-246, April.
    12. Doumpos, Michael & Zopounidis, Constantin, 2011. "Preference disaggregation and statistical learning for multicriteria decision support: A review," European Journal of Operational Research, Elsevier, vol. 209(3), pages 203-214, March.
    13. Eom, Sean B., 1998. "Relationships between the decision support system subspecialities and reference disciplines: An empirical investigation," European Journal of Operational Research, Elsevier, vol. 104(1), pages 31-45, January.
    14. Jacquet-Lagreze, Eric & Siskos, Yannis, 2001. "Preference disaggregation: 20 years of MCDA experience," European Journal of Operational Research, Elsevier, vol. 130(2), pages 233-245, April.

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