Evidential supplier selection based on interval data fusion
  Supplier selection is a typical multi-criteria decision making (MCDM) problem
and lots of uncertain information exist inevitably. To address this issue, a
new method was proposed based on interval data fusion. Our method follows the
original way to generate classical basic probability assignment(BPA) determined
by the distance among the evidences. However, the weights of criteria are kept
as interval numbers to generate interval BPAs and do the fusion of interval
BPAs. Finally, the order is ranked and the decision is made according to the
obtained interval BPAs. In this paper, a numerical example of supplier
selection is applied to verify the feasibility and validity of our method. The
new method is presented aiming at solving multiple-criteria decision-making
problems in which the weights of criteria or experts are described in fuzzy
data like linguistic terms or interval data.
