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Dempster-Shafer evidence theory has been widely applied to solving data fusion problems. However, it is still an open issue about how to combine the evidences effectively when the high conflict evidences are collected. Many scholars have made improvements to solve this problem, but there are new problems such as violation of the theoretical attributes of D-S combination rules and limitations of application scope of improvement methods. Considering these shortcomings, a new evidence synthesis formula based on correlation coefficient of belief functions is proposed in this paper. Our contribution is that the proposed formula can solve the highly conflict issues mentioned above effectively. Moreover, the various types of evidences collected can be well combined. One of the advantages of the proposed model is that conflict coefficient K r is the coefficient of the fusion formula which represents the degree of conflict about evidences. So the fusion process is more flexible and useful. Several examples and comparative experimental simulation are used to illustrate the effectiveness of the proposed methodology.

Multi-attribute decision-making (MADM) is an important part of modern decision-making science. Fuzzy Analytic Hierarchy Process (Fuzzy AHP) is a popular model to deal with the issue of MADM for its flexible and effective advantages. However, The traditional Fuzzy AHP with some limitations does not consider the preference (attitude) of decision makers (DMs). In addition, some ideas of combining Ordered Weighted Average (OWA) and Fuzzy AHP don’t investigated the MADM well. Some programs are only applicable to a few examples, and more general cases do not result in effective decision making. Considering these shortcomings, an OWA-Fuzzy AHP decision model using OWA weights and Fuzzy AHP is proposed in this paper. Our contribution is that the proposed method can handle situations where the degree of fuzzy synthesis is not intersected. Moreover, the loss of information can be reduced in the process of applying the proposed method, so that the decision result is more reasonable than the previous methods. Several examples and comparative experimental simulation are given to illustrate the effectiveness and superiority of the proposed model.

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