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

In this paper, we propose a new interactive procedure for solving multiple objective programming problems. Based upon feed-forward artificial neural networks (FFANNs), the method is called the Interactive FFANN Procedure. In the procedure, the decision maker articulates preference information over r...

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Veröffentlicht in:Management science Jg. 42; H. 6; S. 835 - 849
Hauptverfasser: Sun, Minghe, Stam, Antonie, Steuer, Ralph E
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Linthicum, MD INFORMS 01.06.1996
Institute for Operations Research and the Management Sciences
Schriftenreihe:Management Science
Schlagworte:
Algorithms
analytic hierarchy process
Applied sciences
Artificial intelligence
Artificial neural networks
Computer science; control theory; systems
Connectionism. Neural networks
Connectivity
Decision making
Decision making models
Decision theory. Utility theory
Exact sciences and technology
feed-forward artificial neural networks
Information economics
interactive procedures
Iterative solutions
Linear programming
Management science
Mathematical vectors
Matrices
Methods
multiple criteria decision making
multiple objective programming
Multivariate analysis
Networks
Neural networks
Operational research and scientific management
Operational research. Management science
Operations research
Optimal solutions
Optimization
Programming
Studies
Multicriteria analysis
Decision making
Analytic hierarchy process
Multiobjective programming
Decision criterion
Neural network
Artificial intelligence
Mathematical programming
ISSN:0025-1909, 1526-5501
Online-Zugang:Volltext
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Zusammenfassung:In this paper, we propose a new interactive procedure for solving multiple objective programming problems. Based upon feed-forward artificial neural networks (FFANNs), the method is called the Interactive FFANN Procedure. In the procedure, the decision maker articulates preference information over representative samples from the nondominated 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 Choo [Steuer, R. E., E.-U. Choo. 1983. An interactive weighted Tchebycheff procedure for multiple objective programming. Math. Programming 26 (1) 326–344.]). The computational results indicate that the Interactive FFANN Procedure produces good solutions and is robust with regard to the neural network architecture.
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ISSN:0025-1909
1526-5501
DOI:10.1287/mnsc.42.6.835