Evidential supplier selection based on interval data fusion
This is an incremental improvement for multi-criteria decision-making in supply chain management, specifically addressing fuzzy data like linguistic terms or intervals.
The paper tackles supplier selection under uncertainty by proposing a new method based on interval data fusion, which uses interval basic probability assignments to handle fuzzy criteria weights and ranks options for decision-making, validated through a numerical example.
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.