A Robust Decision-Making Framework for Recruitment Optimization via Hybrid Dombi–Archimedean Operators in Complex q-Rung Orthopair Fuzzy Sets

Abstract

Complex q-Rung Orthopair Fuzzy Sets (Cq-ROFSs) are a powerful tool for handling two-dimensional, periodic uncertain information. The Dombi operator provides a highly flexible parameterized framework for combining fuzzy information through smooth and adjustable t-norm and t-conorm functions. Similarly, the Archimedean operator offers a generalized and consistent mechanism for modeling nonlinear interactions in fuzzy aggregation processes. This paper introduces a novel hybrid approach by combining the Dombi and Archimedean t-norm and t-conorm operations under the Cq-ROF environment. Based on these new operational laws, we develop a suite of aggregation operators: the complex q-Rung orthopair fuzzy Dombi-Archimedean weighted averaging (Cq-ROFDAWA) operator, the complex q-Rung orthopair fuzzy Dombi-Archimedean ordered weighted averaging (Cq-ROFDAOWA) operator, the complex q-Rung orthopair fuzzy Dombi-Archimedean weighted geometric (Cq-ROFDAWG) operator, and the complex q-Rung orthopair fuzzy Dombi-Archimedean ordered weighted geometric (Cq-ROFDAOWG) operator. Key properties of these operators, including idempotency, monotonicity, and boundedness, are rigorously investigated. Furthermore, a robust Multi-Criteria Decision-Making (MCDM) framework is established based on the proposed operators. The model’s reliability and consistency are demonstrated through two distinct scenarios: one with fully unknown criteria weights and another with partially known weights. A numerical example concerning human resource selection is presented to validate the method’s effectiveness and applicability. Finally, a comparative analysis with existing methods underscores the advantages and superiority of the proposed approach.