Evidential supplier selection based on interval data fusion

TL;DR

This paper introduces an evidential data fusion method for supplier selection in multi-criteria decision making, effectively handling uncertain and interval-based data to improve decision accuracy.

Contribution

It proposes a novel interval data fusion approach using interval BPAs, accommodating fuzzy and uncertain criterion weights in supplier selection.

Findings

Method effectively handles uncertain and interval data.

Numerical example demonstrates feasibility and validity.

Improves decision-making accuracy under uncertainty.

Abstract

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.