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Flexible Optimization

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Flexible and Generalized Uncertainty Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 696))

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Abstract

This chapter focuses on flexible optimization. Given that we are able to construct fuzzy relations from target values, fuzzy goals, and from fuzzy relation membership functions, we then have a fuzzy constraint set. The next step in the process is to translate a fuzzy constraint set into a real vector constraint set and to redefine the objective function as a maximization of set belonging. If there are crisp relations and constraints, these remain as part of the real-valued constraint set and augment the translated fuzzy constraint set. If all or part of the original (non-fuzzy goal) objective function is real-valued, then the maximization of set belonging is added to the real-valued objective just as one would do when one adds a new variable to the objective function and constraint for real-valued mathematical programming problems.

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Correspondence to Weldon A. Lodwick .

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Lodwick, W.A., Thipwiwatpotjana, P. (2017). Flexible Optimization. In: Flexible and Generalized Uncertainty Optimization. Studies in Computational Intelligence, vol 696. Springer, Cham. https://doi.org/10.1007/978-3-319-51107-8_5

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  • DOI: https://doi.org/10.1007/978-3-319-51107-8_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51105-4

  • Online ISBN: 978-3-319-51107-8

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