Citation

Wang L, Gong Z. Int. J. Environ. Res. Public Health 2017; 14(10): e14101203.

Affiliation

Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, College of Economics and Management, Nanjing University of Information Science and Technology, Nanjing 210044, China. zwgong26@163.com.

Copyright

(Copyright © 2017, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/ijerph14101203

PMID

28994698

Abstract

As meteorological disaster systems are large complex systems, disaster reduction programs must be based on risk analysis. Consequently, judgment by an expert based on his or her experience (also known as qualitative evaluation) is an important link in meteorological disaster risk assessment. In some complex and non-procedural meteorological disaster risk assessments, a hesitant fuzzy linguistic preference relation (HFLPR) is often used to deal with a situation in which experts may be hesitant while providing preference information of a pairwise comparison of alternatives, that is, the degree of preference of one alternative over another. This study explores hesitation from the perspective of statistical distributions, and obtains an optimal ranking of an HFLPR based on chance-restricted programming, which provides a new approach for hesitant fuzzy optimisation of decision-making in meteorological disaster risk assessments.

Language: en

Keywords

additive consistency; chance-restricted programming; hesitant fuzzy linguistic preference relation (HFLPR); meteorological disaster risk assessment; normal distribution; priority