A cloud decision framework in pure 2-tuple linguistic setting and its application for low-speed wind farm site selection

Abstract

Low-speed wind farm site selection is crucially important for investment returns. However, three great problems reducing the decision-making accuracy and restricting applications exist in the present multiple criteria decision analysis. Firstly, the uncertainty of information fails to be fully described, without considering its randomness. Secondly, during dimensionless treatment and normalizing, some information distortion and loss are caused when evaluating the differences among criteria values just from a mathematical standpoint. Thirdly, the managers are excluded from the decision-making process, which decreases the practicality and operability, and considerably restricts the application of the decision-making methods at the same time. In order to overcome these deficiencies, a cloud-based decision framework under pure 2-tuple linguistic environment is proposed for low-speed wind farm site selection in this paper. First, the criteria values are transformed into 2-tuple linguistic through dimensionless treatment and normalizing; then the extended golden section method is used to transform 2-tuple linguistic into cloud variable. Next, a pure cloud weighted arithmetic averaging operator is constructed to rank the alternatives. After that a case from China is presented to demonstrate the effectiveness. Finally, the comparison analysis and sensitive analysis are conducted, proving the correctness and advantages of the proposed decision framework.