caa a22 4500 606159193 CHVBK 20210128100623.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s00521-014-1813-8 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1813-8 Some two-dimensional uncertain linguistic Heronian mean operators and their application in multiple-attribute decision making [Elektronische Daten] [Yanchang Chu, Peide Liu] Heronian mean (HM) is an important aggregation operator which has the characteristic of capturing the correlations of the aggregated arguments. In this paper, we first analyze the shortcomings of the existing weighted HM operators which do not feature reducibility and idempotency, and then, we propose the new weighted generalized Heronian mean operator and weighted generalized geometric Heronian mean operator, and prove that they can satisfy some desirable properties, such as reducibility, idempotency, monotonicity, and boundedness, and discuss some special cases of these operators. Further, because two-dimensional uncertain linguistic information can easily express the fuzzy information, we propose two-dimensional uncertain linguistic weighted generalized Heronian mean (2DULWGHM) operator and the two-dimensional uncertain linguistic weighted generalized geometric Heronian mean (2DULWGGHM) operator, and some desirable properties and special cases of 2DULWGHM and 2DULWGGHM operators are discussed. Moreover, for multiple-attribute decision-making problems in which attribute values take the form of two-dimensional uncertain linguistic variables, some approaches based on the developed operators are proposed. Finally, we gave an illustrative example to explain the steps of the developed methods and to discuss the influence of different parameters on the decision-making results. The Natural Computing Applications Forum, 2015 Multiple-attribute decision making nationallicence Two-dimensional uncertain linguistic variables nationallicence Heronian mean nationallicence Geometric Heronian mean nationallicence Chu Yanchang School of Economics and Management, Civil Aviation University of China, 300300, Tianjin, China aut Liu Peide School of Economics and Management, Civil Aviation University of China, 300300, Tianjin, China aut Neural Computing and Applications Springer London 26/6(2015-08-01), 1461-1480 0941-0643 26:6<1461 2015 26 521 https://doi.org/10.1007/s00521-014-1813-8 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1813-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Chu Yanchang School of Economics and Management, Civil Aviation University of China, 300300, Tianjin, China aut NATIONALLICENCE 700 1- Liu Peide School of Economics and Management, Civil Aviation University of China, 300300, Tianjin, China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/6(2015-08-01), 1461-1480 0941-0643 26:6<1461 2015 26 521