摘要：In many circumstances, decisions are based on subjective experience. However, some views can be vague, meaning that policymakers do not know exactly how they should express their opinions. Therefore, it is necessary for researchers to provide scientific decision frameworks, among which the multi-criteria decision making (MCDM) method in the linguistic environment is gradually favored by scholars. A large body of literature reports relevant approaches with regard to linguistic term sets, but existing approaches are insufficient to express the subjective thoughts of policymakers in a complex and uncertain environment. In this paper, we address this problem by introducing the concept of evidential linguistic term set (ELTS). ELTS generalizes many other uncertainty representations under linguistic context, such as fuzzy sets, probabilities, or possibility distributions. Measures on ELTS, such as uncertainty measure, dissimilarity measure and expectation function, provide general frameworks to handle uncertain information. Modeling and reasoning of information expressed by ELTSs are realized by the proposed aggregation operators. Subsequently, this paper presents a novel MCDM approach called evidential linguistic ELECTRE method, and applies it to the case of selection of emergency shelter sites. The findings demonstrate the effectiveness of the proposed method for MCDM problems under linguistic context and highlight the significance of the developed ELTS.

关键词：Evidential linguistic term set;Belief function theory;Multi-criteria decision making ;Uncertainty modeling ;ELECTRE;Emergency shelter site selection

基金资助：This research was supported by the grants from the Natural Science Foundation of Shandong Province of China (Grant No. ZR2023QG099), Guangxi Social Science Fund (22BGL005), and the Social Risk Governance Research of Huangdao Second Jiaozhou Bay Tunnel Construction (SK210471).

DOI:10.1007/s10462-024-10709-2

原文刊载于：THE ARTIFICIAL INTELLIGENCE REVIEW, MAR 1 2024

WOS链接：https://webofscience.clarivate.cn/wos/alldb/full-record/WOS:001172098000003