Abstract:
This paper presents a robust mathematical model for task prioritization under conditions of multicriteria complexity, changing input parameters, and partial data incompleteness—common challenges in modern distributed and streaming digital environments. The proposed model automatically calculates criterion weights based on statistical variability (e.g., standard deviation) and dynamically adjusts them using feedback from task execution outcomes. Unlike traditional approaches such as AHP and TOPSIS – which require complete data and manual parameter tuning—the model is resistant to missing values, interpretable, and does not rely on retraining or imputation. A compensation mechanism for incomplete data and adaptation to changing feature structures is incorporated, ensuring consistent performance in fragmented and asynchronous information contexts. Comparative evaluation with machine learning models and heuristic methods shows that the proposed approach achieves high ranking accuracy (via Spearman correlation), stability under up to 50% missing data, and linear scalability as the number of tasks and criteria increases. Experimental results on synthetic and semi-real datasets confirm its practical effectiveness. The model is applicable in a wide range of digital platforms, including decision support systems, DevOps, logistics, monitoring, and incident management, especially where adaptability and transparency are critical under uncertainty and dynamic change.
Keywords:prioritization, multicriteria, incomplete data, adaptation, robustness, data stream.
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