Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Granular computing: an emerging paradigm
Granular computing: an emerging paradigm
A fuzzy decision model for conceptual design
Systems Engineering
Introduction to Operations Research and Revised CD-ROM 8
Introduction to Operations Research and Revised CD-ROM 8
On the soundness of altering granular information
International Journal of Approximate Reasoning
An intelligent method for selecting optimal materials and its application
Advanced Engineering Informatics
Handbook of Granular Computing
Handbook of Granular Computing
Discussion: From imprecise to granular probabilities
Fuzzy Sets and Systems
Toward a generalized theory of uncertainty (GTU)--an outline
Information Sciences: an International Journal
Simulation of cutting force using nonstationary Gaussian process
Journal of Intelligent Manufacturing
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Real-life decision problems are often solved by using a very limited set of data points. The computational complexity of such decision problems can be handled easily by using a mathematical entity called stochastic point cloud (SPC). In general, SPC is a form of granular information and is simulated by a data-driven stochastic process. In this study, particular interests are given on normal-distribution-driven SPCs. When the parameters of a normally distributed variable is not clearly known due to the lack of information or due to the multiplicity of regression analysis, it forms a set of SPCs. Three types of such SPCs are described in detail in this study. The effectiveness of such SPCs in solving a real-life decision problem (i.e., how to minimize vehicle emissions in Abu Dhabi Emirate of United Arab Emirates) is also shown. To develop more realistic and man-machine-friendly decision support systems one can use SPCs.