A new interactive multiple objective programming procedure is developed that combines the strengths of the Interactive Weighted Tchebycheff Procedure (Steuer and Choo 1983) and the Interactive FFANN Procedure (Sun, Stam and Steuer 1993). In this new procedure, nondominated trial solutions are generated by solving Augmented Weighted Tchebycheff Programs (Steuer 1986), based on which the decision maker articulates his/her preference information by assigning "values" to these solutions or by making pairwise comparisons. The elicited preference information is used to train a feed-forward artificial neural network, which in turn is used to screen new trial solutions for presentation to decision maker in the next iteration. Computational results are reported, comparing the current procedure with the Interactive Weighted Tchebycheff Procedure and the Interactive FFANN Procedure. The results show that this new procedure yields good quality solutions
