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Large-scale unconventional emergencies, such as earthquake, hurricane and tsunami, usually require crucial decisions. These emergency problems often involve many experts from various fields such as geology, seismology, and meteorology, and government department officers due to the nature of complexity and widely affected scope. In this regard, group decision making can be used to solve these problems. Additionally, an important characteristic of unconventional emergencies is that they vary rapidly. A single decision is often unable to adapt to the rapidly changing situation. Considering these factors, this study aims to develop a multi-stage group decision-making model to solve emergency problems. Firstly, we develop a method to determine the weights of stages and use the Partitioning Around Medoids clustering algorithm to classify experts. Then, we introduce two consensus measures, namely type α consensus and type γ consensus, to select the best alternative and find the total ranking list based on mining consensus sequences. Finally, an illustrative example concerning the typhoon “Lekima” is provided to illustrate the applicability of our proposed model.

Type α and type γ consensus for multi-stage emergency group decision making based on mining consensus sequences