Shadowed AHP for multi-criteria supplier selection

TL;DR

This paper introduces a novel Shadowed AHP method that effectively handles multi-granular uncertain preference data in multi-criteria supplier selection, enhancing decision-making accuracy.

Contribution

The paper proposes a new Shadowed AHP approach using shadowed fuzzy numbers to unify and process multi-granular uncertain preferences in decision-making.

Findings

Successfully applied to supplier selection problem

Improves handling of multi-granular uncertain data

Provides a new ranking method for preferences

Abstract

Numerous techniques of multi-criteria decision-making (MCDM) have been proposed in a variety of business domains. One of the well-known methods is the Analytical Hierarchical Process (AHP). Various uncertain numbers are commonly used to represent preference values in AHP problems. In the case of multi-granularity linguistic information, several methods have been proposed to address this type of AHP problem. This paper introduces a novel method to solve this problem using shadowed fuzzy numbers (SFNs). These numbers are characterized by approximating different types of fuzzy numbers and preserving their uncertainty properties. The new Shadowed AHP method is proposed to handle preference values which are represented by multi-types of uncertain numbers. The new approach converts multi-granular preference values into unified model of shadowed fuzzy numbers and utilizes their properties. A new ranking approach is introduced to order the results of aggregation preferences. The new approach is applied to solve a supplier selection problem in which multi-granular information are used. The features of the new approach are significant for decision-making applications.