IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v42y1996i6p835-849.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.42.6.835
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.42.6.835?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Minghe Sun, 2003. "Procedures for Finding Nondominated Solutions for Multiple Objective Network Programming Problems," Transportation Science, INFORMS, vol. 37(2), pages 139-152, May.
    10. 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.
    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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:42:y:1996:i:6:p:835-849. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.