A feedforward neural network for multiple criteria decision making

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 determing the most desirable alternative. This paper is to demonstrate the potential role of artificial neural networks for multiple criteria decision making. It 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, the 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.