Abstract
Mathematical programming is a widely used tool in Operational Research. The first — and usually the most difficult — step of problem solving requires formulation of the model: finding the mathematical description of the relationships between decisions and their outcomes. The researcher is faced with the problem of finding the proper functions and their parameters. This kind of problems is dealt with using approximation theory.
The paper presents theorems and assumptions for using neural networks as approximators of functions in formulation of the optimization models Thanks to neural networks’ approximation capabilities the difficulties connected with finding the functional forms of relationships and their parameters can be avoided.
The method of identification of parameters is illustrated with an exemplary application in the area of bank credit portfolio optimization.
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© 2002 Springer-Verlag Berlin Heidelberg
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Gąsiorowski, P. (2002). On Optimization Model Formulation Using Neural Networks. In: Trzaskalik, T., Michnik, J. (eds) Multiple Objective and Goal Programming. Advances in Soft Computing, vol 12. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1812-3_22
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DOI: https://doi.org/10.1007/978-3-7908-1812-3_22
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1409-5
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