Affiliations: [a] School of Economics and Management, Shanghai Maritime University, Shanghai, China
| [b] School of Business, Liaocheng University, Liaocheng, China
Correspondence:
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Corresponding author. Mingshuo Cao, School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China. E-mail: caomingshuo0606@163.com.
Abstract: This article proposes a novel multi-criteria group decision making (MCGDM) approach with multi-granularity hesitant fuzzy linguistic term set (HFLTS). It consists three aspects: (1) The processing algorithms for multi-granularity HFLTS; (2) G-DEMATEL model based on HFLTS; (3) A consensus model with the feedback mechanism. To do that, the relative projection model for multi-granular hesitant fuzzy language information is presented and the similarity degree between individual decision matrices based on relative projection is defined. On the basis, the similarity degree is used to determine the expert’s weight vectors, and the SD-MGHIOWA operator is defined to aggregate the experts opinions. The traditional G-DEMATEL model is improved by the multi-granular hesitant fuzzy language and a new model is built to analyze the correlation between criterion and weight vector. Furthermore, consensus degree is defined from three levels to identify the inconsistent experts, and a feedback mechanism is activated to generate recommendation advice for the inconsistent experts to increase consensus degree. After that, a comprehensive score mechanism of alternatives is designed to select the most appropriate alternative after the consensus is reached. The main characteristics of the proposed MCGDM is that it not only considers the correlation between criterions but also provides the consensus model with the feedback mechanism in the context of hesitant fuzzy language. Finally, an example is provided to illustrate the feasibility and effectiveness of the developed method, which are then compared to the existing methods.
