Development of a decision support system using advanced multi-criteria decision-making techniques

Abstract

The object of research is decision-making processes in conditions of uncertainty, with an emphasis on improving the accuracy and reliability of multi-criteria decision-making methods. The problem to be solved is the difficulty of making reliable and optimal decisions in dynamic environments where data variability, incomplete information, and subjective judgments pose significant challenges. Traditional methods often fail to adequately address these complexities, leading to suboptimal or unreliable outcomes.

The essence of the results lies in the creation of a DSS (Decision Support System) that leverages Z-number TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to combine performance metrics with confidence levels, providing a more comprehensive framework for decision-making. The system is uniquely suited to prioritize alternatives effectively, even when faced with high levels of uncertainty and variability in input data. Due to its features and characteristics, the DSS allows for greater adaptability and precision in decision-making, ensuring results that are not only accurate but also reliable. The explanation for these results lies in Z-number TOPSIS’s ability to integrate quantitative analysis with the evaluation of data reliability, making it far more effective than traditional MCDM (Multi Criteria Decision Making) techniques. A systematic comparison with other methods, such as traditional TOPSIS and Fuzzy TOPSIS, demonstrates that Z-number TOPSIS consistently outperforms these approaches, particularly in scenarios involving dynamic and uncertain conditions. The study contributes to the advancement of decision-making methodologies by providing insights into how uncertainty can be systematically incorporated into ranking models. A comparative analysis with traditional TOPSIS and Fuzzy TOPSIS shows that Z-number TOPSIS outperforms these methods, providing a 10 % improvement in consistency under noisy data conditions and a 15 % better adaptability under conflicting criteria scenarios.

The results are applicable in fields such as supply chain management, where decision-makers must optimize inventory distribution and supplier selection under fluctuating demand, healthcare, where prioritization of patient treatment is required under resource constraints, and financial risk assessment, where investment decisions depend on uncertain economic conditions. The findings highlight the potential of Z-number TOPSIS in supporting more reliable and adaptable decision-making processes in complex and uncertain environments.