期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 254, 期 2, 页码 610-621出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ejor.2016.04.001
关键词
Decision analysis; Qualitative decision making involving risk appetites; Generality asymmetric linguistic term set; Asymmetric sigmoid semantics; Value-at-risk fitting approach
资金
- National Natural Science Foundation of China [71301141, 71561026, 71571123, 61273209]
- Humanity and Social Science Youth Foundation of Ministry of Education of China [13YJC630247]
- China Postdoctoral Science Foundation [2015M570792]
- Science Foundation and Major Project of Educational Committee of Yunnan Province [2014Z100]
- Applied Basic Research Programs of Science and Technology Commission of Yunnan Province [2013FD029]
The linguistic term set is an applicable and flexible technique in qualitative decision making (QDM). To further develop the linguistic term set, this paper proposes a generalized asymmetric linguistic term set (GALTS) based on the asymmetric sigmoid semantics, which belongs to an asymmetric and non-uniform linguistic term set, and can be used to address the QDM problems involving risk appetites of the decision maker (DM). Then, a value-at-risk fitting (VARF) approach is designed for obtaining the risk appetite parameters of the GALTS and six desirable properties of the GALTS are analyzed, i.e., asymmetry, non-uniformity, generality, variability, range consistency, and diminishing-utility. Based on the above approaches and the generalized asymmetric linguistic preference relations (GALPRs), a QDM process involving risk appetites of the DM is designed. Because the GALPRs consist of subjective information provided by the DM, the process is not perfectly consistent and is usually difficult to change or repeat. Thus, a transitivity improvement approach is investigated, and the corresponding calculation steps are provided. Finally, an example dealing with the problem of investment decision making is provided, and the results fully demonstrate the validity of the proposed methods for QDM involving risk appetites. (C) 2016 Elsevier B.V. All rights reserved.
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