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DOI number:10.1007/s10489-024-05992-z
Journal:Applied Intelligence(中科院二区)
Key Words:Large-scale multi-attribute emergency group decision-making · Clustering · Evidential reasoning · Graph attention mechanism · Consensus reaching process
Abstract:Emergency decision-making problem is common in our daily life. To solve this kind of problem, a group of decision-makers (DMs) are usually invited to make a decision in a limited time. Since multiple attributes are usually considered, it's called large-scale multi-attribute emergency group decision-making (LS-MA-EGDM). There are two issues in the general research of LS-MA-EGDM. First, clustering and consensus-reaching process (CRP) should consider the influence of DMs’intrinsic features. Second, consensus adjustment within and among sub-clusters ought to be fast to prevent multi-round iteration. Accordingly, (1) we introduce graph attention mechanism to calculate the attention coefficients between DM pair's intrinsic features. The multi-head graph attention coefficient based on social network analysis (SNA) is proposed, which is then combined with opinion similarity to construct a social network clustering method. (2) The Einstein product operator is introduced to propagate the attention coefficients and yield DMs’ weights, which is then incorporated in the subsequent adjustment allocation. (3) Identification rules are provided based on four consensus types in the CRP. The one-iteration personalized adjustment strategies corresponding to different consensus types are then proposed. (4) Evidential reasoning (ER) algorithm is finally utilized to aggregate the preferences of clusters after consensus is reaching. The proposed method is further applied to a chemical plant explosion in Texas to illustrate its effectiveness and validity in dealing with emergencies.
Co-author:Ying Zhang,Xin‑Yu Fan,Ting Wu,Ba‑Yi Cheng,Jian Wu
First Author:Mi Zhou（通讯作者）
Indexed by:Journal paper
Discipline:Management Science
Document Type:J
Volume:55
Page Number:453
Translation or Not:no
Date of Publication:2025-02-15
Included Journals:SCI