Designing a Decision Support System for Project Evaluation Using Z-TOPSIS
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Alish Nazarov
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The object of this research is the development of a decision support system (DSS) for multi-criteria evaluation under uncertainty. The study addresses a key problem: traditional decision-making methods, such as deterministic TOPSIS, fail to effectively account for the inherent uncertainty and reliability issues in real-world data. This inadequacy creates significant challenges in domains where both quantitative and qualitative uncertainties are critical, such as renewable energy planning and resource allocation. The essence of the results lies in the proposed hybrid Z-TOPSIS framework, which integrates Z-numbers a mathematical tool designed to model both the value and reliability of data into the conventional TOPSIS method. This integration allows the framework to provide more accurate and reliable decision-making outcomes by considering not only the values of decision criteria but also the confidence associated with those values. These features enable the proposed system to handle uncertainty comprehensively, significantly improving its effectiveness over traditional deterministic approaches. These results were achieved due to the unique characteristics of Z-numbers, which reflect real-world complexities more effectively than traditional deterministic models. By modeling subjective judgments and reliability in tandem, Z-numbers enhance the decision-making process, ensuring resilient evaluations even with limited or uncertain data. The proposed DSS is particularly suitable for use in fields like renewable energy planning, urban development, and other domains requiring resilient decision-making under uncertainty. The system’s adaptability and reliability make it a valuable tool for addressing complex, real-world decision-making scenarios, ensuring transparency, confidence, and practicality in its applications.
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Authors
Alish Nazarov
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References:
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