Evidential Supplier Selection Based on Interval Data Fusion

Abstract

Supplier selection is a common and important problem, which is usually modeled in a multi-criterion decision-making framework. In this framework, multiple criteria need to be determined and an expert team needs to be constructed. Generally, different criteria and experts always hold different weights. Due to the complexity of the practical problems, sometimes lots of fuzzy and uncertain information exists inevitably and the weights are tough to determine accurately. Interval number is a simple but effective method to handle the uncertainty. However, most current papers transform an interval number to a crisp number to make decisions, which causes loss of information more or less. To address this issue, a new evidential method based on interval data fusion is proposed in this paper. Dempster–Shafer evidence theory is a widely used method in a supplier selection problem due to its powerful ability in handling uncertainty. In our method, the criteria and decision makers are weighted in the form of interval numbers to generate interval basic probability probabilities, which represent the supporting degree to an event in Dempster–Shafer evidence theory. The obtained interval basic probabilities assignments are fused to make a final decision. Due to the properties of interval data, our method has its own specific advantages, like losing less information, providing different strategies for decision making and so on. The new method is proposed aiming at solving multi-criterion decision-making problems in which the reliability of decision makers or weights of criteria are described in the form of fuzzy data like linguistic terms or interval data. A numerical example of supplier selection is used to illustrate our method. Also, the results and comparison show the correctness and effectiveness of the new interval data fusing evidential method.