# A high reliability based evidential reasoning approach

## TL;DR

This paper introduces a high-reliability decision-making method for multi-attribute analysis when exact weights are uncertain.

## Contribution

A novel evidential reasoning approach that prioritizes reliability in decision-making under uncertain attribute weights.

## Key findings

- The method identifies the best alternative based on single or multiple attribute weight sets.
- It measures reliability and generates rankings based on optimal weight intervals and evaluation grades.
- The approach was successfully applied to automobile performance evaluation.

## Abstract

Attribute weights exert a significant effect on the solution in multi-attribute decision analysis (MADA), since solutions produced by varying attribute weights probably vary. When a decision maker has inadequate valid data, understanding or experience to produce exact attribute weights, he/she perhaps wants to seek a solution with highest reliability, referred to in this study as a highly reliable solution. To this end, a high-reliability evidential reasoning (ER) approach is put forward in the present work, which achieves alternatives comparison through determination of their reliability relative to attribute weights under ER scenario. Initially, the best alternative supported by single or multiple sets of attribute weights was determined. Then, reliability estimation is given for every alternative. In the case of highest reliability, the optimal interval of attribute weights and evaluation grades between the optimal alternative is measured and their ranking is generated. The proposed approach to the process is based on a combination of identifying these alternatives and measuring their reliability. The problem of automobile performance evaluation is explored, finding that the proposed approach is capable of effectively generating high reliability solutions for MADA problems.