An emergency decision making method based on the multiplicative consistency of probabilistic linguistic preference relations

作者：Gao, Jie; Xu, Zeshui^*; Ren, Peijia; Liao, Huchang

来源：International Journal of Machine Learning and Cybernetics, 2019, 10(7): 1613-1629.

DOI：10.1007/s13042-018-0839-0

• Emergency decision making
• Probabilistic linguistic preference relations (PLPRs)
• Multiplicative consistency
• Probability correction

As the evolution of emergencies is often uncertain, it may lead to multiple emergency scenarios. According to the characteristics of emergency management, this paper proposes an emergency decision support method by using the probabilistic linguistic preference relations (PLPRs) whose elements are the pairwise comparisons of alternatives given by the decision-makers (DMs) in the form of probabilistic linguistic term sets (PLTSs). As the decision data are limited, it is difficult for the DMs to provide exact occurrence probabilities of all possible emergency scenarios. Thus, we propose a probability correction method by using the computer-aided tool named the case-based reasoning (CBR) to obtain more accurate and reasonable occurrence probabilities of the probabilistic linguistic elements (PLEs). Then, we introduce a multiplicative consistency index to judge whether a PLPR is consistent or not. Afterwards, an acceptable multiplicative consistency-based emergency decision support method is proposed to get more reliable results. Furthermore, a case study about the emergency decision making in a petrochemical plant fire accident is conducted to illustrate the proposed method. Finally, some comparative analyses are performed to demonstrate the feasibility and effectiveness of the proposed method.

• 出版日期2019-7
• 单位四川大学; 南京信息工程大学