An Influence Network-Based Consensus Model for Large-Scale Group Decision Making with Linguistic Information

Abstract

The vast majority of the existing social network-based group decision-making models require extra information such as trust/distrust, influence and so on. However, in practical decision-making process, it is difficult to get additional information apart from opinions of decision makers. For large-scale group decision making (LSGDM) problem in which decision makers articulate their preferences in the form of comparative linguistic expressions, this paper proposes a consensus model based on an influence network which is inferred directly from preference information. First, a modified agglomerative hierarchical clustering algorithm is developed to detect subgroups in LSGDM problem with flexible linguistic information. Meanwhile, a measure method of group consensus level is proposed and the optimal clustering level can be determined. Second, according to the preference information of group members, influence network is constructed by determining intra-cluster and inter-cluster influence relationships. Third, a two-stage feedback mechanism guided by influence network is established for the consensus reaching process, which adopts cluster adjustment strategy and individual adjustment strategy depending on the different levels of group consensus. The proposed mechanism can not only effectively improve the efficiency of consensus reaching of LSGDM, but also take individual preference adjustment into account. Finally, the feasibility and effectiveness of the proposed method are verified by the case of intelligent environmental protection project location decision.

The Multi-Type Demands Oriented Framework for Flex-Route Transit Design

“…Then, HCA [43,44] is adopted to divide two clusters based on the standardized fea- Based on historical demands, we obtain multiple kinds of station features and input these features into MFCM to solve the classification problem. In addition, we extract regular travel patterns from historical demands, which can reinforce the service capacity for static-station-based demands.…”

“…Then, HCA [43,44] is adopted to divide two clusters based on the standardized features above. The division includes three steps as follows.…”