Abstract
Expert reliability is the ability to make unmistakable evaluations on attributes for the performance of an alternative in multiattribute group decision making (MAGDM). It has a significant effect on the group consensus calculation and group decision-making; unfortunately the reliability has not been considered in the consensus-reaching model yet. This study focuses on providing a reliability-based consensus model for MAGDM with analytically evidential reasoning (analytical ER for short) approach. The basic probability assignment (BPA) function which can be discounted by expert reliability is introduced to describe the performance judgments of each expert, by combining which of the group judgments could be determined with analytical ER rule. Then the consensus degrees of three levels (attribute level, alternative level, and expert level) are defined by Jousselme distance to identify the experts who should revise their judgments and point out revised suggestions, based on which a decision-making method within interaction is proposed to determine the effective BPA functions of all experts and make final decision-making. Finally, a numerical case study is carried out to illustrate the effectiveness of the method.
Highlights
In multiattribute group decision-making (MAGDM), a group of experts make an evaluation on alternatives by several attributes and interact with each other to derive a common solution [1]
Expert reliability has a significant effect on the group consensus calculation and group decision-making; it has not been considered in the consensus-reaching model of MAGDM yet
This study aims at providing a reliability-based consensus model for MAGDM with analytically evidential reasoning (ER) approach
Summary
In multiattribute group decision-making (MAGDM), a group of experts make an evaluation on alternatives by several attributes and interact with each other to derive a common solution [1]. In order to assist with group interaction consensus model, three aspects of researches have been proposed as follows. The first aspect focuses on applying the fuzziness tools to construct consensus models. The fuzziness tools such as fuzzy theory [3,4,5,6,7,8,9,10], hesitant fuzzy set [11,12,13,14,15], and linguistic/preference information [12, 13, 15,16,17,18,19,20,21,22] were introduced into consensus model to extract experts’ subjective judgments. Dynamic consensus model [27,28,29,30], consensus models considering social networks [2, 19, 22], soft consensus model [5], adaptive consensus model [20, 30], and interactive consensus model [2, 31] were proposed to make consensus models play better roles to meet different
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