MAGDM Framework Based on Double Hierarchy Bipolar Hesitant Fuzzy Linguistic Information and Its Application to Optimal Selection of Talents

• Published in: International journal of fuzzy systems Vol. 24; no. 4; pp. 1757 - 1779
• Main Authors: Liu, Peide, Shen, Mengjiao, Pedrycz, Witold
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2022; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Intelligence; Decision making; Engineering; Fuzzy sets; Linguistics; Management Science; Methods; Operations Research; Personnel selection; Optimal selection of talents; SWARA method; Extended WASPAS method; Double hierarchy hesitant bipolar fuzzy linguistic term sets; Framework; Multiple-attribute group decision-making
• ISSN: 1562-2479; 2199-3211
• DOI: 10.1007/s40815-021-01231-6

Hesitant fuzzy linguistic term sets (HFLTSs) and double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) are two frequently used linguistic information forms in uncertain decision-making environments. However, they only include membership grades and cannot yield fuzzy information from a negative aspect. A bipolar fuzzy set can quantify evaluation information from positive and negative sides using positive and negative memberships, respectively. To address this issue, double hierarchy bipolar hesitant fuzzy linguistic term sets (DHBHFLTSs) are proposed, which can highlight the importance of the negative membership degree, and the objects can be evaluated from positive and negative aspects. Furthermore, DHBHFLTSs increase the reasonableness and comprehension of the evaluation information in the process of optimal talent selection. This paper proposed a framework involving the stepwise weight assessment ratio analysis (SWARA) method and the extended weighted aggregated sum product assessment (WASPAS) method. The extended WASPAS method is utilized to aggregate the evaluation information of all the alternatives under the DHBHFLTSs context. So, this proposed method increases the ranking accuracy. The SWARA method is extended to DHBHFLTSs to rank and determine the criteria. This weight determination method is helpful for coordinating and gathering data from experts. Therefore, the proposed method can obtain the weight values efficiently. Subsequently, a case of talent selection is utilized to show the feasibility and applicability of the proposed framework. Finally, the accuracy and comparison analyses with other methods illustrate the superiority of this framework.