Some Construction Methods of Aggregation Operators in Decision-Making Problems: An Overview

Abstract

Aggregating data is the main line of any discipline dealing with fusion of information from the knowledge-based systems to decision-making. The purpose of aggregation methods is to convert a list of objects, all belonging to a given set, into a single representative object of the same set usually by an n-ary function, so-called aggregation operator. As the useful aggregation functions for modeling real-life problems are limited, the basic problem is to construct a proper aggregation operator, usually a symmetric one, for each situation. During the last decades, a number of construction methods for aggregation functions have been developed to build new classes based on the existing well-known operators. There are three main construction methods in common use: transformation, composition, and convex combination. This paper compares these methods with respect to the type of aggregating problems that can be handled by each of them.

REFS-A Risk Evaluation Framework on Supply Chain

“…In the study conducted by the authors in [35][36][37], various aggregating functions were examined. Aggregation functions necessitate several conditions [38,39], including validity, monotonicity, sensitivity, symmetricity, linearity, scale fit, and scale endpoint identity.…”