Dependent Interval 2-Tuple Linguistic Aggregation Operators and Their Application to Multiple Attribute Group Decision Making

Autor:	Qing-Lian Lin, Jing Wu, Hu-Chen Liu
Rok vydání:	2014
Předmět:	Degree (graph theory) Interval (mathematics) Linguistics Group decision-making Operator (computer programming) Rule-based machine translation Artificial Intelligence Control and Systems Engineering Tuple Preference (economics) Software Information Systems Mathematics Multiple attribute
Zdroj:	International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 22:717-735
ISSN:	1793-6411 0218-4885
DOI:	10.1142/s0218488514500366
Popis:	Consider the various types of uncertain preference information provided by the decision makers and the importance of determining the associated weights for the aggregation operator, the multiple attribute group decision making (MAGDM) methods based on some dependent interval 2-tuple linguistic aggregation operators are proposed in this paper. Firstly some operational laws and possibility degree of interval 2-tuple linguistic variables are introduced. Then, we develop a dependent interval 2-tuple weighted averaging (DITWA) operator and a dependent interval 2-tuple weighted geometric (DITWG) operator, in which the associated weights only depend on the aggregated interval 2-tuple arguments and can relieve the influence of unfair arguments on the aggregated results by assigning low weights to them. Based on the DITWA and the DITWG operators, some approaches for multiple attribute group decision making with interval 2-tuple linguistic information are proposed. Finally, an illustrative example is given to demonstrate the practicality and effectiveness of the proposed approaches.
Databáze:	OpenAIRE
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