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

Author

Listed:
  • Minghe Sun

    (Division of Management and Marketing, College of Business, University of Texas at San Antonio, San Antonio, Texas 78249)

  • Antonie Stam

    (International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria and Department of Management, Terry College of Business, University of Georgia, Athens, Georgia 30602)

  • Ralph E. Steuer

    (Faculty of Management Science, Brooks Hall, University of Georgia, Athens, Georgia 30602)

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.

Suggested Citation

  • Minghe Sun & Antonie Stam & Ralph E. Steuer, 1996. "Solving Multiple Objective Programming Problems Using Feed-Forward Artificial Neural Networks: The Interactive FFANN Procedure," Management Science, INFORMS, vol. 42(6), pages 835-849, June.
    • Handle: RePEc:inm:ormnsc:v:42:y:1996:i:6:p:835-849
    DOI: 10.1287/mnsc.42.6.835
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    Citations

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    Cited by:

    1. C Gagné & M Gravel & W L Price, 2005. "Using metaheuristic compromise programming for the solution of multiple-objective scheduling problems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(6), pages 687-698, June.
    2. Minghe Sun, 2005. "Warm-Start Routines for Solving Augmented Weighted Tchebycheff Network Programs in Multiple-Objective Network Programming," INFORMS Journal on Computing, INFORMS, vol. 17(4), pages 422-437, November.
    3. Golmohammadi, Davood, 2011. "Neural network application for fuzzy multi-criteria decision making problems," International Journal of Production Economics, Elsevier, vol. 131(2), pages 490-504, June.
    4. Gal, Tomas & Hanne, Thomas, 2006. "Nonessential objectives within network approaches for MCDM," European Journal of Operational Research, Elsevier, vol. 168(2), pages 584-592, January.
    5. Sadeghi, Mehdi & Ameli, Ahmad, 2012. "An AHP decision making model for optimal allocation of energy subsidy among socio-economic subsectors in Iran," Energy Policy, Elsevier, vol. 45(C), pages 24-32.
    6. Minghe Sun, 2003. "Procedures for Finding Nondominated Solutions for Multiple Objective Network Programming Problems," Transportation Science, INFORMS, vol. 37(2), pages 139-152, May.
    7. I Horowitz, 2003. "Preference-neutral attribute weights in the journal-ranking problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(5), pages 452-457, May.
    8. Sun, Minghe, 2005. "Some issues in measuring and reporting solution quality of interactive multiple objective programming procedures," European Journal of Operational Research, Elsevier, vol. 162(2), pages 468-483, April.
    9. Mingue SUn, 2010. "A Branch-and-Bound Algorithm for Representative Integer Efficient Solutions in Multiple Objective Network Programming Problems," Working Papers 0007, College of Business, University of Texas at San Antonio.
    10. Doumpos, Michael & Zopounidis, Constantin, 2004. "Developing sorting models using preference disaggregation analysis: An experimental investigation," European Journal of Operational Research, Elsevier, vol. 154(3), pages 585-598, May.
    11. Matthias Ehrgott & Xavier Gandibleux, 2004. "Approximative solution methods for multiobjective combinatorial optimization," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(1), pages 1-63, June.
    12. Doumpos, Michael & Zopounidis, Constantin, 2011. "Preference disaggregation and statistical learning for multicriteria decision support: A review," European Journal of Operational Research, Elsevier, vol. 209(3), pages 203-214, March.

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