Cloud Model-Based Decision-Making in Uncertain Environments

Cloud model-based decision-making offers a unified framework to handle both the randomness and the fuzziness inherent in complex systems. By transforming qualitative concepts into quantitative representations, the cloud model bridges statistical properties and linguistic assessments, enabling decision makers to capture uncertainty more faithfully than with traditional probability or fuzzy set approaches alone. Characterised by numerical descriptors—expectation, entropy and hyper-entropy—it supplies a means to express the central tendency, the dispersion of data and the stability of uncertainty. In practice, cloud models have been integrated with multi-criteria decision techniques such as Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and dynamic weighting schemes to enhance risk assessment, performance evaluation and strategic planning. Applications span mental-load evaluation in high-risk operations, food-safety risk assessment in complex supply chains and process safety analysis in industrial systems. The adaptability of the model to both small-sample scenarios and large data sets, along with its capacity for bidirectional transformation between qualitative judgments and quantitative measures, underscores its global significance in sectors from energy infrastructure and food security to public health and environmental management.

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Cloud Model-Based Decision-Making in Uncertain Environments publication trend

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Technical terms

Cloud model: A mathematical construct conveying the uncertainty transition between qualitative concepts and quantitative data using three numerical descriptors.

Expectation (Ex): The central value or mean of a cloud, indicating the most representative quantitative output for a qualitative concept.

Entropy (En): A measure of the dispersion or fuzziness within the cloud model, reflecting the degree of uncertainty of the qualitative concept.

Hyper-entropy (He): The uncertainty of the entropy itself, quantifying the stability of the fuzziness and randomness represented in the cloud.

Analytic Hierarchy Process (AHP): A structured decision methodology that decomposes a problem into a hierarchy of criteria and alternatives, assigning relative weights through pairwise comparisons.

TOPSIS: A ranking technique that identifies solutions closest to the ideal and furthest from the nadir by evaluating geometric distances in a multi-criteria context.

References

1. Quantification study of mental load state based on AHP–TOPSIS integration extended with cloud model: methodological and experimental research. Complex & Intelligent Systems (2023).
2. A Rice Hazards Risk Assessment Method for a Rice Processing Chain Based on a Multidimensional Trapezoidal Cloud Model. Foods (2023).
3. A Novel Risk Matrix Approach Based on Cloud Model for Risk Assessment Under Uncertainty. IEEE Access (2021).