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 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Linthicum, MD
INFORMS
01.06.1996
Institute for Operations Research and the Management Sciences |
| Schriftenreihe: | Management Science |
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| ISSN: | 0025-1909, 1526-5501 |
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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 (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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| AbstractList | A new interactive procedure is proposed 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 nondominated set either by assigning preference values to the sample solution or by making pairwise comparisons in a fashion similar to that in the analytical 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. In addition, the procedure is compared computationally with the Tchebycheff method (Steuer and Choo 1983). The computational results indicate that the interactive FFANN procedure produces good solutions and is robust with regard to the neural network architecture. 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. 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. 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 1983). The computational results indicate that the Interactive FFANN Procedure procedures good solutions and is robust with regard to the neural network architecture. 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. |
| Author | Stam, Antonie Sun, Minghe Steuer, Ralph E |
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| Copyright | Copyright 1996 Institute for Operations Research and the Management Sciences 1996 INIST-CNRS Copyright Institute of Management Sciences Jun 1996 |
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| Snippet | In this paper, we propose a new interactive procedure for solving multiple objective programming problems. Based upon feed-forward artificial neural networks... A new interactive procedure is proposed for solving multiple objective programming problems. Based upon feed-forward artificial neural networks (FFANN), the... |
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| SubjectTerms | 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 |
| Title | Solving Multiple Objective Programming Problems Using Feed-Forward Artificial Neural Networks: The Interactive FFANN Procedure |
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