Abstract
This paper presents an intelligent decision support system based on neural network technology for multicriteria model selection. This paper categorizes the problem as simple, utility / value, interactive and outranking type of problem according to six basic features. The classification of the problem is realized based on a two-step neural network analysis applying back-propagation algorithm. The first Artificial Neural Network (ANN) model that is used for the selection of an appropriate solving method cluster consists of one hidden layer. The six input neurons of the model represent the MCDM problem features while the two output neurons represent the four MCDM categories. The second ANN model is used for the selection of a specific method within the selected cluster.
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Ulengin, F., Ilker Topcu, Y., Onsel Sahin, S. (2001). An Artificial Neural Network Approach to Multicriteria Model Selection. In: Köksalan, M., Zionts, S. (eds) Multiple Criteria Decision Making in the New Millennium. Lecture Notes in Economics and Mathematical Systems, vol 507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56680-6_9
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DOI: https://doi.org/10.1007/978-3-642-56680-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42377-5
Online ISBN: 978-3-642-56680-6
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