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

Sun M, Stam A, & Steuer RE (1996). Solving Multiple Objective Programming Problems Using Feed-forward Artificial Neural Networks: The Interactive FFANN Procedure of Innovation. IIASA Research Report (Reprint). IIASA, Laxenburg, Austria: RR-97-014. Reprinted from Management Science, 42(6) [June 1996].

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Abstract

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.

Item Type: Monograph (IIASA Research Report (Reprint))
Research Programs: Management Coordination and Development (MCD)
Bibliographic Reference: Reprinted from Management Science; 42(6) [June 1996]
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 02:07
Last Modified: 19 Jul 2016 07:12
URI: http://pure.iiasa.ac.at/4846

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