Journal
APPLIED SOFT COMPUTING
Volume 77, Issue -, Pages 155-175Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2019.01.009
Keywords
Multi-attribute group decision making; Probabilistic linguistic term sets; Operational laws; Aggregating operators
Categories
Funding
- National Natural Science Foundation of China [71740021, 11861034, 11461030]
- Thirteen five'' Programming Project of Jiangxi province Social Science [17GL33, 18GL13]
- Science and Tecnology Project of Jiangxi Province Educational Department of China [GJJ180946]
- Jiangxi Provincial Humanities and Social Sciences Research Project [JC162020]
Ask authors/readers for more resources
No technology, no finance'' has been the consensus in banking industry. Under the background of financial technology (Fintech), how to select an appropriate technology company to cooperate for the banks has become a key. The technology company selection can be regarded as a kind of multi-attribute group decision making (MAGDM) problems. The probabilistic linguistic term set (PLTS) is a useful tool to express decision makers' (DMs') evaluations in the technology company selection. This paper develops a new method for MAGDM with PLTSs. Firstly, the possibility degree and range value of PLTSs are defined. Then a possibility degree algorithm is designed for ranking PLTSs. An Euclidean distance measure between PLTSs is presented and extended to probabilistic linguistic matrices. Based on Archimedean t-norm and s-norm, some new operational laws for PLTSs are defined and some desirable properties are discussed. Then, a generalized probabilistic linguistic Hamacher weighted averaging (GPLHWA) operator and a generalized probabilistic linguistic Hamacher ordered weighted averaging (GPLHOWA) operator are developed. Some useful properties for these operators are investigated in detail. Combined with the subjective weights of DMs, the DMs' weights are determined by the adjusted coefficients. Using the GPLHWA operator, the collective decision matrix is obtained by aggregating all the individual decision matrices. By maximizing the total weighted square possibility degree, a multi-objective program is constructed to derive the attribute weights. The ranking order of alternatives is generated by integrating ELECTRE and TOPSIS. Thereby, a new method is put forward for MAGDM with PLTSs. A Fintech example is analyzed to show the effectiveness of the proposed method. The sensitivity analysis and comparative analyses are conducted to illustrate its advantages. (C) 2019 Elsevier B.V. All rights reserved.
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