Interactive multiple objective programming using Tchebycheff programs and artificial neural networks

A new interactive multiple objective programming procedure is developed that combines the strengths of the interactive weighted Tchebycheff procedure and the interactive FFANN procedure. In this new procedure, nondominated solutions are generated by solving augmented weighted Tchebycheff programs. The decision maker indicates preference information by assigning ‘values’ to or by making pairwise comparisons among these solutions. The revealed preference information is then used to train a feed-forward artificial neural network. The trained feed-forward artificial neural network is used to screen new solutions for presentation to the decision maker on the next iteration. The computational experiments, comparing the current procedure with the interactive weighted Tchebycheff procedure and the interactive FFANN procedure, produced encouraging results.