Linguistic Pythagorean fuzzy Aczel–Alsina aggregation operators and their application in multi-attribute group decision-making: An approach for smart city development

作者：Shahid Hussain Gurmani, Z. Rasool, Rifaqat Ali, Shehr Bano, Huayou Chen, Yuming Feng, Rana Muhammad Zulqarnain · 发表于：Engineering Applications of Artificial Intelligence · 年份：2025 · DOI：10.1016/j.engappai.2025.111215 · 被引用次数：12 · 研究领域：Multi-Criteria Decision Making、Cognitive Science and Mapping、Rough Sets and Fuzzy Logic

This paper aims to present a new multi-attribute group decision-making (MAGDM) approach for solving problems in an uncertain and complex environment. In any MAGDM problem, the key challenge is how to quantify and aggregate the objective uncertainty information contained in the data. To answer this, in this paper, we utilize the idea of linguistic Pythagorean fuzzy set (LP y FS) to describe the information in terms of linguistic membership degree (LMD) and linguistic non-membership degree (LNMD). We first present some new operations of LP y FS, including Aczel–Alsina (AA) sum, AA product, and AA scalar multiplication. At that point, we develop various LP y F aggregation operators which are based on these defined operations, including the LP y F Aczel–Alsina weighted averaging (LP y FAAWA) operator, the LP y F Aczel–Alsina ordered weighted averaging (LP y FAAOWA) operator, LP y F Aczel–Alsina weighted geometric (LP y FAAWG) operator and LP y F Aczel–Alsina ordered weighted geometric (LP y FAAOWG) operator. We configure these operators with different characteristics. It can be seen that the proposed operators have commutativity, monotonicity, boundary, and idempotency properties. Then, we design a new technique dependent on these operators to solve MAGDM problems. The applicability of the established approach is demonstrated through a case study related to smart city development. The result demonstrates the new technique's feasibility and applicability. In the end, sensitivity a...