Complex Intuitionistic Hesitant Fuzzy Aggregation Information and Their Application in Decision Making Problems

Abstract

In the realm of decision-making, the delineation of uncertainty and ambiguity within data is a pivotal challenge.This study introduces a novel approach through complex intuitive hesitant fuzzy sets (CIHFS), which offers a unique multidimensional perspective for data analysis.The CIHFS framework is predicated on the concept that membership degrees reside within the unit disc of the complex plane, thereby providing a more nuanced representation of data.This method stands apart in its ability to simultaneously process and analyze data in a two-dimensional format, incorporating additional descriptive elements known as phase terms into the membership degrees.The study is bifurcated into two primary phases.Initially, a possibility degree measure is proposed, facilitating the ranking of numerical values within the CIHFS context.Subsequently, the development of innovative operational rules and aggregation operators (AOs) is undertaken.These AOs are instrumental in amalgamating diverse options within a CIHFS framework.The research dissects and deliberates on various AOs, including weighted average (WA), ordered weighted average (OWA), weighted geometric (WG), ordered weighted geometric (OWG), hybrid average (HA), and hybrid geometric (HG).Furthermore, the study extends to the realm of multi-criteria decision making (MCDM), where it proposes a methodology utilizing intricate intuitive and fuzzy information.This methodology emphasizes the objective management of weights, thereby enhancing the decision-making process.The study's findings hold significant implications for the optimization of resources and decision-making strategies, providing a robust framework for the application of CIHFS in practical scenarios.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 38 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.