Decision-making model with fuzzy preference relations based on consistency local adjustment strategy and DEA

As one of the most useful tools, fuzzy preference relations (FPRs) can cope with the situations in which the experts are more comfortable providing their evaluation information with numerical values. Consistency-improving process and deriving the reliable priority weight vector for alternatives are two significant and challenging issues in decision making with FPRs. This paper investigates a novel decision-making model with FPRs on the basis of consistency local adjustment strategy and data envelopment analysis (DEA). Firstly, a new approach is proposed to generate the multiplicative consistent FPRs. Subsequently, a convergent consistency-improving algorithm for FPRs is developed to transform the unacceptable multiplicative consistent FPRs into the acceptable ones. In the consistency-improving process for FPRs, the local adjustment strategy is presented to employ decision-maker original evaluation information sufficiently. In order to determine the priority weight vector for alternatives, a novel fuzzy DEA model is constructed. Furthermore, a decision-making model with FPRs is designed to derive the reliable decision-making results. Finally, a numerical example of selecting the most important influence factor for fog–haze is provided, and the comparison with existing approaches is made to validate the rationality and effectiveness of the developed model.