Robust Sensitivity Analysis for Multi-Attribute Deterministic Hierarchical Value Models
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There is a wide array of multi-attribute decision analysis methods and associated sensitivity analysis procedures in the literature. However, there is no detailed discussion of sensitivity analysis methods solely relating to additive hierarchical value models. The currently available methodology in the literature is unsophisticated and can be hard to implement into complex models. The methodology proposed in this research builds mathematical foundations for a robust sensitivity analysis approach and extends the current methodology to a more powerful form. The new methodology is easy to impleme...
There is a wide array of multi-attribute decision analysis methods and associated sensitivity analysis procedures in the literature. However, there is no detailed discussion of sensitivity analysis methods solely relating to additive hierarchical value models. The currently available methodology in the literature is unsophisticated and can be hard to implement into complex models. The methodology proposed in this research builds mathematical foundations for a robust sensitivity analysis approach and extends the current methodology to a more powerful form. The new methodology is easy to implement into complex hierarchical value models and gives flexible and dynamic capabilities to decision makers during sensitivity analysis. The mathematical notation is provided in this study along with applied examples to demonstrate this methodology. Global and local sensitivity analysis are considered and implemented using the proposed robust technique. This research provides consistency and a common standard for the decision analysis community for sensitivity analysis of multi-attribute deterministic hierarchical value models.
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