In this article, we combine the framework of multi-attribute utility theory, the perspective of set-based design, and the explicit mathematical representation of imprecision into a single approach to conceptual design.
We lack evidence to decide whether this mistake reflects a temporary reasoning glitch, a deeper misunderstanding of the relevant scaling concepts, and/or a tendency toward imprecision about mathematical details (reflected also, perhaps, in her glossing over the distance-squared dependence in Fick's law).
RST is an extension of set theory, consisting of a mathematical model for uncertainty and imprecision handling, knowledge representation, and rough classification.
The fuzzy set theory was proposed by Lotfi A. Zadeh in 1965 to solve and model uncertainty, ambiguity, imprecisions, and vagueness using mathematical and empirical models [19].
Fuzzy goal programming (FGP) is a mathematical decision-making mechanism to incorporate uncertainty and imprecision into the formulation.
This logic provides a mathematical approach to interpret the grey zones of imprecision.
As explained in detail in many works in the field of fuzzy differential equations, uncertainty has to be considered in the formulation of a mathematical model for a better adequacy, due to the imprecision inherent to the information available or the behavior of the dynamical system itself (see, for instance, [3]).
Different kind of difficulties arise in dealing with the uncertainties and imprecision either already existing in the data or due to the mathematical tools used to solve the model featuring various situations.
Traditional mathematical modelling techniques have been used to compute the numbers that have the least imprecision.
Data imprecision can be addressed through optimization approaches such as fuzzy, stochastic and interval mathematical programming (Wilson and Baetz, 2001ab; Huang et al. 2005ab; El Hanandeh and El-Zein, 2010; Fan and Huang, 2012).
