A dynamic complex intuitionistic fuzzy Dombi framework for multi-attribute decision-making with IoT applications

Real-world environments are usually characterized by uncertainty, hesitation, and time-dependent information in decision-making problems. The current fuzzy and intuitionistic fuzzy models have the limitation that they cannot adequately model multidimensional and dynamic uncertainty without loss of information. To overcome this shortcoming, this paper develops a new dynamic multi-attribute decision-making model using complex intuitionistic fuzzy sets. A more powerful score function is initially suggested to address the comparison ambiguity that is inherent in traditional complex intuitionistic fuzzy ranking approaches. Based on this enhancement, two dynamic Dombi aggregation operators, i.e., the complex intuitionistic fuzzy dynamic Dombi weighted averaging and complex intuitionistic fuzzy dynamic Dombi weighted geometric operators, are proposed to effectively aggregate time-dependent decision information. The structural properties of the proposed operators, including closure, idempotency, monotonicity, and boundedness, are rigorously established. A systematic decision-making algorithm is then constructed under the proposed framework. The practicality and effectiveness of the approach are demonstrated through a case study involving the selection of an optimal Internet of Things platform. Comparative and sensitivity analyses confirm that the proposed methods provide stable, reliable, and more discriminative results than existing approaches. The developed framework offers a flexible and robust tool for dynamic decision-making problems in complex and uncertain environments.