Name
Title | ||
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Research on credit risk evaluation of online supply chain finance with triangular fuzzy information. |
Abstract | ||
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In recent years, with the rapid development of e-commerce, supply chain finance has gradually become a major focus of competition in the electricity supplier. Into the financial sector for e-commerce can not only bring a new source of profit for companies, but also for their suppliers to strengthen the enhanced viscosity, channel control, upstream and downstream enterprises to achieve common prosperity month core business provides an effective way. But there is a financial product supply chain risks, it can be said that the risk is the center of the supply chain financial products; same time, supply chain financial risks in financial services between customers and themes affecting the interests of both sides. Therefore, the strengthening of financial risk management is a very important issue currently facing. In this paper, we investigate the multiple attribute decision making problems for evaluating the credit risk of online supply chain finance with triangular fuzzy information. Then, we have developed the triangular fuzzy induced Einstein ordered weighted geometric (TFIEOWG) operator. We have used the TFIEOWG operator to multiple attribute decision making for evaluating the credit risk of online supply chain finance with triangular fuzzy information. Finally, an example is proposed to show the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3233/JIFS-179253 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | Field | DocType |
Multiple attribute decision making,triangular fuzzy number,credit risk,online supply chain finance | Fuzzy logic,Operations research,Artificial intelligence,Supply chain,Credit risk,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
37 | 2.0 | 1064-1246 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||