Abstract
As one of the important components of global land ecosystem, rangeland ecosystem has important value of ecosystem services. With the degeneration of rangeland in recent years, sustainability within rangeland ecosystem has become an increasingly important issue. The aim of this paper is to develop a novel dynamic decision-making approach based on hesitant fuzzy information to evaluate rangeland sustainability that considers ecological, social and economic aspects. Firstly, a modified satisfaction degree of alternative is presented, based on which a mathematical model for determining the stage weights is constructed. Secondly, the compromise ratio method (CRM), whose basic principle is that the optimal alternative should have the nearest distance from positive ideal solution and the longest distance from negative ideal solution simultaneously, is extended to accommodate hesitant fuzzy environment, and then adopted to tackle the dynamic decision-making with hesitant fuzzy information. Compared with the existing methods, the proposed method can eliminate the impact of attribute magnitude and dimension. Lastly, a numerical example on the evaluation of rangelands is provided to illustrate the practicality and superiority of the proposed method.
Highlights
Rangeland, which is one of the most important ecological barriers, plays an important part in human existence and social development
Liu et al compromise ratio method (CRM)-based dynamic decision-making with hesitant fuzzy information
Through the analysis it can be found that problems existing in the dynamic decision-making with hesitant fuzzy information are in the following: (1) the variety of adjacent stages is taken into account, the technique for determining the stage weights requires strict hypothesis (Liao et al, 2014)
Summary
Rangeland, which is one of the most important ecological barriers, plays an important part in human existence and social development. Through the analysis it can be found that problems existing in the dynamic decision-making with hesitant fuzzy information are in the following: (1) the variety of adjacent stages is taken into account, the technique for determining the stage weights requires strict hypothesis (Liao et al, 2014).
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