An Intelligent Computational Framework for Multi-Criteria Decision Analysis under Uncertainty

Abstract

Uncertainty, vagueness, and inadequate knowledge sometimes make it hard to make decisions in complicated real-world situations. This research provides an intelligent computational paradigm for multi-criteria decision analysis under uncertainty to address these problems.The suggested approach combines rough set theory with advanced methods for modelling uncertainty to accurately show decision information that is unclear or contradictory. A structured decision-support method is created to assess and prioritize many choices according to a range of criteria that may be conflicting.A sustainable system selection problem, which takes into account technical, environmental, and economic factors all at once, shows how the proposed framework can be used. The experimental findings demonstrate that the suggested methodology improves decision robustness, interpretability, and reliability in comparison to current decision-making models. The suggested intelligent computational framework can be adapted for various decision-support and artificial intelligence applications functioning in uncertain environments, due to its flexibility and universality.