Stochastic-integration-based decision support methods for heterogeneous multi-attribute group decision making with several attribute sets

Li, Weiwei; Yi, Pingtao; Zhang, Danning; Rajmohan, Saravan; Dong, Qiankun

doi:10.1016/j.eswa.2023.121100

Cited by 3 publications

(3 citation statements)

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“…The existing research on HMAGDM can be classified into two frameworks: heterogeneous type of evaluation information [4,5,6] and heterogeneous sets of attributes and/or alternatives. The second framework can be subdivided into two cases: one is that only the attribute set is heterogeneous [7,8,9,10,11,12]; The other is that the attribute set and alternative set are both heterogeneous [13,14]. At present, research on HMAGDM with heterogeneous sets of attributes and alternatives is relatively insufficient.…”

Section: Introductionmentioning

confidence: 99%

“…(2) In existing HMAGDM research, weights are mostly subjectively given by experts and lack objectivity [7,8,9,10]. Moreover, the objective weighting methods currently used are too simple [11,12,13,14], and the obtained weights lack comprehensiveness, which is not conducive to obtaining reasonable decision results.…”

Section: Introductionmentioning

confidence: 99%

“…(3) Most HMAGDM methods use weighted aggregation [7,10,11,12,13] or TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods [8,9] to rank the alternatives. However, both of them do not consider the utility of group and individuals simultaneously and cannot well reflect the decision-maker's subjective preferences.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Heterogeneous Multi-Attribute Group Decision-Making Integrating Multi-Granulation Weighting Model and Improved VIKOR in Uncertain Linguistic Environment

Pang,

Song,

Wang

et al. 2023

Fuzzy Systems and Data Mining IX

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Heterogeneous multi-attribute group decision-making (HMAGDM) is a complex decision-making problem that widely exists in the real world. However, there is relatively little research on the HMAGDM problems when the attribute set and alternative set are both heterogeneous, and the existing studies still have some limitations, such as the weight calculation is too simple, lacking objectivity and comprehensiveness; the ranking methods does not consider the utility of both group and individuals simultaneously, lacking flxibility and practicality. In order to obtain more effective decision results, a HMAGDM method integrating multi-granulation weighting model and improved VIKOR in uncertain linguistic environment is proposed in this paper. Our contributions can be identified as follows: (1) On the basis of the uncertainty and closeness of uncertain linguistic terms (ULTs), a measure indicator for the effectiveness of experts’ opinions is proposed, and a finest-granulation weight optimization model for experts is established by maximizing the effectiveness; (2) Based on comprehensive consideration of effectiveness and deviation, a bi-objective optimization model is proposed to obtain the multi-granulation weights of attributes; (3) An improved VIKOR method combining the boundedness of ULTs and the multi-granulation weights of attributes is proposed to obtain more stable and effective ranking results. Finally, the case study and comparative analysis illustrate the feasibility and characteristics of the proposed method.

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