A feedforward neural network for multiple criteria decision making

Jun Wang, B. Malakooti

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)


Many complex real-world problems are characterized as decision making with multiple, conflicting and noncommensurate objectives. Because of the complexity of factors that are involved, it is usually difficult to derive a decision rule for determining the most desirable alternative. This paper is to demonstrate the potential role of artificial neural networks for multiple criteria decision making. This paper presents a feedforward neural network for solving discrete multiple criteria decision problems under certainty. Starting with formulating multiple criteria decision problems under the theme of supervised learning, this paper specifies two types of multiattribute decision models, proposes a particular form of feedforward neural network, analyzes some desirable properties associated with supervised learning, presents an improved learning algorithm and discusses results of illustrative examples and numerical simulation.

Original languageEnglish
Pages (from-to)151-167
Number of pages17
JournalComputers and Operations Research
Issue number2
Publication statusPublished - Feb 1992
Externally publishedYes


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