Solving Multiple Objective Programming Problems Using Feed-forward Artificial Neural Networks: The Interactive FFANN Procedure of Innovation

In this paper we propose a new interactive procedure for solving multiple objective programming problems. Based upon feed-forward artificial neural networks (FFANN), the method is called the Interactive FFANN Procedure. In the procedure, the decision maker articulates preference information over representative samples from the non-dominated set either by assigning preference "values" to the sample solutions or by making pairwise comparisons in a fashion similar to that in the Analytic Hierarchy Process. With this information, a FFANN is trained to represent the decision maker's preference structure. Then, using the FFANN, an optimization problem is solved to search for improved solutions. An example is given to illustrate the Interactive FFANN Procedure. Also, the procedure is compared computationally with the Tchebycheff Method (Steuer and Cho 1983). The computational results indicate that the Interactive FFANN Procedure produces good solutions and is robust with regard to the neural network architecture