Dual strategies consensus reaching process for ranking consensus based probabilistic linguistic multi-criteria group decision-making method

Abstract

Group consensus is critical in multi-criteria group decision-making (MCGDM). However, the extant consensus reaching process (CRP) methods focus on the consensus on the decision matrices rather than the rankings of alternatives. This paper proposes the dual strategies CRP for ranking consensus based probabilistic linguistic MCGDM. According to the decision matrices provided by decision makers, the rankings of alternatives are generated. Then, we define ranking similarity degree based on the individual alternative rankings and opinion similarity degree based on the decision matrices, respectively. Based on the ranking similarity degrees, the group consensus index (GCI) is defined and the dual strategies CRP are proposed. In the proposed CRP method, the first strategy pays attention to DMs with low ranking similarity degree but high opinion similarity degree, while the second strategy in CRP concentrates on DMs with low ranking similarity degree and low opinion similarity degree. The first strategy constructs minimum adjustment programming model while the second strategy directly provide adjustment advice according to the reference evaluation. These two strategies both aims to change the rankings of alternatives by adjusting the evaluations in the decision matrices. At length, an actual example is presented to demonstrate the effectiveness of the erected method and comparison analyses clarify its advantages and superiorities.