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An Overview of Flexible and Generalized Uncertainty 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, some of whose ideas we have taken from [1,2,3], develops flexible and generalized uncertainty optimization where the salient feature of the problem is decision making under gradual set belonging. As we have mentioned in Chap. 1, optimization problems are normative processes that embody the idea of order since we must measure how one outcome is “better” than another. This requires an order. That is, “best” is a normative criterion for optimization that requires an order that measures “best”. The real number system contains within itself a complete order or we might say that the real number system is what we mean by a complete order. We use the real numbers, \( \mathbb {R} ,\) onto which the normative criteria “best” of flexible and generalized uncertainty optimization is mapped.

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Authors and Affiliations

  1. Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, USA

    Weldon A. Lodwick

  2. Department of Science and Technology, Federal University of São Paulo, São Paulo, Brazil

    Luiz L. Salles-Neto

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  1. Weldon A. Lodwick
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  2. Luiz L. Salles-Neto
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Correspondence to Weldon A. Lodwick .

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Lodwick, W.A., Salles-Neto, L.L. (2021). An Overview of Flexible and Generalized Uncertainty Optimization. In: Flexible and Generalized Uncertainty Optimization. Studies in Computational Intelligence, vol 696. Springer, Cham. https://doi.org/10.1007/978-3-030-61180-4_4

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