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<p>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.</p>

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A novel consensus reaching approach for large-scale multi-attribute emergency group decision-making under social network clustering based on graph attention mechanism

  • 期刊论文

  • 发表日期：

    2025年2月15日

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  • Mi Zhou,
  • Ying Zhang,
  • Xin-Yu Fan,
  • Ting Wu,
  • Ba-Yi Cheng,
  • Jian Wu

摘要

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.