Ph.D. Mellado-Cid, Cristhian

Intuitionistic Fuzzy Sets (IFS) and Pythagorean Fuzzy Sets (PFS) offer robust frameworks for handling uncertainty and vagueness in decision-making. This paper presents a novel chi-square similarity measure designed specifically for IFS and PFS. Traditional similarity measures often struggle to adequately handle the complexities inherent in these fuzzy set models. Through various numerical discussions spanning diverse domains, we demonstrate the effectiveness and versatility of the proposed measure. The case studies conducted illustrate its superiority over existing similarity measures in capturing nuanced relationships within fuzzy sets, offering valuable insights for decision-makers operating in uncertain environments. This research significantly contributes to advancing similarity measures in fuzzy set theory, providing practical tools to navigate complex decision-making scenarios.

Interval numbers are widely used in many fields to provide information about different scenarios. This paper presents several new uncertain average formulations using the ordered weighted average, prioritized, probabilistic and induced operators. First, the work introduces the uncertain prioritized induced probabilistic ordered weighted average (UPIPOWA) operator that its main applicability is in complex group decision making problems. Also, a wide range of special cases and extensions using quasi-arithmetic means are presented, such is the case of the quasi-arithmetic UPIPOWA (QUPIPOWA) operator. The study analyzes the applicability of this new approach in economic variables, specifically are imports and exports. Particularly, the paper focuses on measuring the imports and exports for Latin America for 2017