Abstract
In many practical situations, we need to optimize under fuzzy constraints. There is a known Bellman-Zadeh approach for solving such problems, but the resulting solution, in general, depends on the choice of a not well-defined constant M. We show that this dependence disappears if we use an algebraic t-norm (and-operation) \( f_ \& (a,b)=a\cdot b\), and we also prove that the algebraic product is the only t-norm for which the corresponding solution is independent on M.
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Acknowledgements
This work was supported in part by the National Science Foundation grants 0953339, HRD-0734825 and HRD-1242122 (Cyber-ShARE Center of Excellence) and DUE-0926721, by Grants 1 T36 GM078000-01 and 1R43TR000173-01 from the National Institutes of Health, and by a grant N62909-12-1-7039 from the Office of Naval Research.
This work was performed when Juan Carlos Figueroa-García was a visiting researcher at the University of Texas at El Paso.
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Figueroa-García, J.C., Ceberio, M., Kreinovich, V. (2018). Algebraic Product is the only T-norm for Which Optimization Under Fuzzy Constraints is Scale-Invariant. In: Ceberio, M., Kreinovich, V. (eds) Constraint Programming and Decision Making: Theory and Applications. Studies in Systems, Decision and Control, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-319-61753-4_8
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DOI: https://doi.org/10.1007/978-3-319-61753-4_8
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