An integrated MCDM framework based on interval 2-tuple linguistic: A case of offshore wind farm site selection in China

Abstract

Offshore wind farm (OWF) site selection is critical to the successful development of offshore wind energy and manifests as a complex multi-criteria decision-making (MCDM) process. To promote the further healthy development of offshore wind energy, this study developed a new integrated MCDM framework to better evaluate and rank OWF sites. The main contributions are as follows. First, the interval 2-tuple linguistic (I2TL) provides a simple, interpretable, and precise approach to linguistic information handling and effectively prevents information missing and distortion. Second, different opinions are aggregated using a modified similarity aggregation method (SAM), reducing the errors due to neglecting the effect of individual differences on consistency. Third, a new integrated weighting method combining the simplified best-worst method (SBWM) and method based on the removal effects of criteria (MEREC) is adopted to acquire evaluation criteria weights, which comprehensively reveals the relative importance of criteria. Fourth, the proximity indexed value (PIV) method is expanded with I2TL for alternative ranking to minimize the rank reversal problem. Finally, the applicability and robustness of the proposed framework are validated through a case study in China as well as sensitivity and comparative analyses. The proposed MCDM framework can provide beneficial support for managers to analyze and select the optimal OWF site.