INTELLIGENT DECISION-SUPPORT SYSTEM FOR EVALUATION AND OPTIMIZATION OF WEB RESOURCES WITHIN URBAN INFORMATION INFRASTRUCTURE BASED ON FUZZY MCDM MODELS
DOI:
https://doi.org/10.32782/2786-9024/v4i6(38).359133Keywords:
intelligent systems, fuzzy logic, multi-criteria decision making, DEMATEL-DANP- VIKOR, decision-support system, urban information infrastructure, web resource evaluation.Abstract
The purpose of this study is to develop and substantiate a fuzzy hybrid decision-support model for evaluating and optimizing web resources operating within the urban information infrastructure. The study aims to formalize the assessment of technical, behavioral, semantic, and algorithmic factors influencing web resource performance while accounting for expert uncertainty and dynamic changes in digital environments. The research is based on a combined methodological framework integrating machine learning (ML) and natural language processing (NLP) techniques with fuzzy multi- criteria decision-making (MCDM) methods, specifically DEMATEL-DANP-VIKOR. Fuzzy expert evaluation is implemented using linguistic scales transformed into triangular fuzzy numbers, followed by defuzzification through the centroid method. The Fuzzy DEMATEL approach is applied to construct the interrelationship matrix and identify cause-effect dependencies among evaluation criteria. DANP is used to determine criterion weights, and VIKOR is employed to calculate integral efficiency indices and rank alternative web resources. An infological model of the information system is developed to represent structured functional modules and information flows, including data acquisition, fuzzy expert assessment, ML analytics, multi-criteria optimization, and recommendation generation subsystems. The proposed framework enables the formal quantification of interdependencies among technical, content-related, behavioral, and semantic criteria influencing web resource effectiveness. The integration of fuzzy logic reduces subjectivity in expert assessments and allows uncertainty to be incorporated into the evaluation process. The model supports the computation of integral performance indicators and the identification of priority directions for optimization. The developed infological model ensures systemic consistency of analytical, computational, and managerial components within the decision-support environment. The scientific novelty consists in the integration of fuzzy expert evaluation with network-based MCDM methods (DEMATEL-DANP-VIKOR) and machine learning analytics within a unified formal decision- support framework. Unlike traditional optimization approaches, the proposed model simultaneously accounts for causal relationships among criteria, uncertainty of expert judgments, and adaptive data-driven analysis. The proposed model can be implemented within urban digital infrastructures to enhance the efficiency, accessibility, and transparency of municipal web services. The approach provides a foundation for developing adaptive intelligent platforms capable of maintaining stability and operational effectiveness under evolving technological and informational conditions.
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