A 2-dimensional uncertain linguistic MABAC method for multiattribute group decision-making problems

Peide Liu,Dongyang Wang

doi:10.1007/s40747-021-00372-3

Peide Liu, Dongyang Wang

Open Access

https://doi.org/10.1007/s40747-021-00372-3

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Abstract

The 2-dimensional uncertain linguistic variable (2DULV) can depict decision-makers’ subjective assessments on the reliability of given evaluation results, which is a valid and practical tool to express decision information. In this study, we develop an improved MABAC method with 2DULVs to handle multiattribute group decision-making (MAGDM) problems where the weight information of attributes is unknown. First, some related theories of 2DULVs and the basic procedure of the MABAC method are briefly reviewed. Then, the maximum comprehensive evaluation value method is extended to 2DULVs to obtain combination weights of attributes, in which the subjective weights are determined according to the best–worst method (BWM) and the objective weights are calculated by the maximum deviation method. Besides, the generalized weighted average operator for 2DULVs (2DULGWA) is utilized to aggregate the evaluation information given by all experts. Finally, an improved MABAC for 2DULVs (2DUL-MABAC) is proposed, and an example is carried out to explain the validity of the proposed approach.

Highlights

Multiattribute group decision-making (MAGDM) is a process to rank alternatives based on multiple attributes by several decision-makers (DMs), which has been widely used in engineering, economy, management, and military, such as green supplier selection [43], stock investment evaluation [35], selection of financial technologies [23], evaluation in design projects [12], and so forth [2, 36, 44]
Liu [14] extended it to the 2-dimensional uncertain linguistic variables (2DULVs) where both I and II class linguistic terms (LT) are replaced by uncertain LTs (ULTs)
This paper proposes the 2DUL-MABAC method for MAGDM problems with unknown attribute weights

Summary

Introduction

Multiattribute group decision-making (MAGDM) is a process to rank alternatives based on multiple attributes by several decision-makers (DMs), which has been widely used in engineering, economy, management, and military, such as green supplier selection [43], stock investment evaluation [35], selection of financial technologies [23], evaluation in design projects [12], and so forth [2, 36, 44]. In some more complex situations, owing to the limitation of time or knowledge, uncertain LTs [42] or interval-valued intuitionistic LTs [38] are used. There is another kind of MAGDM problem in real life, such as the evaluation of science and technology projects and blind reviews of doctoral dissertations, etc. DMs need to give the result of the evaluation indicators, and give a reliability result for the given evaluation results in the form of ‘familiarity degree’ Considering these points, Zhu et al [50] put forward the 2-dimensional expression model, that is, the I and II class LTs are simultaneously used to express the evaluation values. The 2DULV is very helpful to express uncertain and ambiguous decisionmaking information, and many aggregation operators were developed based on it, including power generalized weighted aggregation operator [21], generalized hybrid aggregation operators [18], density generalized hybrid weighted

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