Abstract
To address complexity information fusion problems involving fuzzy and grey uncertainty information, we develop prioritized averaging aggregation operator and Bonferroni mean aggregation operator with grey linguistic 2-tuple variables and apply them to design a new decision-making scheme. First, the grey linguistic 2-tuple prioritized averaging (GLTPA) operator is developed to characterize the prioritization relationship among experts and employed to fuse experts’ information into an overall opinion. Second, we establish dual generalized grey linguistic 2-tuple weighted Bonferroni mean (DGGLTWBM) operator to capture the interrelationship among any attribute subsets, which can be reduced to some conventional operators by adjusting parameter vector. On that basis, a flexible group decision-making approach with fuzzy and grey information is designed and applied to an evaluation problem, in which grey relationship analysis (GRA) method and a linear programming model are combined to extract attribute weights from partially known attribute information. Furthermore, an illustrative example is employed to illustrate the practicality and flexibility of the designed method by conducting the related comparative studies.
Highlights
Multiple-attribute decision-making (MADM) methods support us to identify the solution from some possible alternatives, which are widely used in our daily life such as human resource selection [1], investment strategy analysis [2, 3], task scheduling and sort management [4], supplier identifying [5], query processing [6], and location planning voting system [7].With the increasing uncertainty and complexity in managerial decision practices, it is usually difficult to express their preferences in the form of an accurate value
Inspired by dual generalized weighted Bonferroni mean (DGWBM) operator [36], we develop a dual generalized grey linguistic 2-tuple weighted Bonferroni mean (DGGLTWBM) operator
We develop a dual generalized grey linguistic 2-tuple weighted Bonferroni mean (DGGLTWBM) operator by incorporating the DGWBM operator (Definition 7) into a grey linguistic 2-tuple situation
Summary
Multiple-attribute decision-making (MADM) methods support us to identify the solution from some possible alternatives, which are widely used in our daily life such as human resource selection [1], investment strategy analysis [2, 3], task scheduling and sort management [4], supplier identifying [5], query processing [6], and location planning voting system [7]. With the increasing uncertainty and complexity in managerial decision practices, it is usually difficult to express their preferences in the form of an accurate value The reason for this problem is mainly derived from the cognitive uncertainty (fuzzy uncertainty) and insufficient information (grey uncertainty). We develop PA (prioritized averaging) operator for grey linguistic 2-tuple terms to serve as a way of reflecting the priority relationship that existed in decision-makers. Based on the developed aggregation operators, we design a new technique for solving group decision problems with grey linguistic 2tuple variables, in which grey relationship analysis (GRA) method and linear programming approach are combined to optimize attribute weights from a priori weight information.
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