Many group decision making (GDM) models enable experts to use only one preference information representation form. It is natural to allow experts to express preferences in various formats considering the heterogeneity of experts. In this case, how to reach the consensus of a group from heterogeneous preference information is an attractive research issue. This study proposes a consensus reaching process for large‐scale GDM with heterogeneous preference information. First, we review various preference formats including preference orderings, numerical assessments, interval‐valued assessments, and linguistic assessments. To facilitate the heterogeneous information aggregation, we classify experts into subgroups according to their preference types rather than the similarities of preference values, and then aggregate the homogeneous preference values in each subgroup. The subgroup priorities derived by homogeneous methods are then aggregated into global priorities. An ordinal consensus measuring process based on individual orderings is introduced. To reach the ordinal consensus, optimization models are constructed to ensure each subgroup's preferences equivalent to the global preferences, and the recommended ranges and strength of preference modification are given to experts. Finally, the proposed method is validated by an illustrative example about blockchain platform selection.

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Since energy information in nodes can comprehensively reflect the reactive injection and voltage level of node, this paper proposes a new partitioning method based on static energy function and multi-threshold search algorithm for decentralized voltage control. With the energy information of node, the node energy correlation degree index (NECDI) is proposed to evaluate reactive relevance between different nodes. Based on the NECDI, the energy sensitivity matrix (ESM) is then established. After that, the ESM is decomposed to get voltage control area (VCR) by multi-threshold search method. Finally, plenty of simulation results about IEEE 30-and IEEE 118bus systems, and some comparisons with the traditional partitioning method demonstrate the validity of our proposed algorithm, which is effective to solve some questions of the practical engineering projects. Key word: static energy function; energy information; node energy correlation degree index; energy sensitivity matrix; multi-threshold search I.

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