A New Approach for Multicriteria Assessment in the Ranking of Alternatives Using Cardinal and Ordinal Data

TL;DR

This paper introduces a novel multi-criteria assessment method combining two Virtual Gap Analysis models to improve the evaluation of alternatives using both quantitative and qualitative data, enhancing fairness and accuracy.

Contribution

The paper presents a new MCA approach that integrates two VGA models based on linear programming to better handle mixed data types and reduce subjective bias in decision-making.

Findings

Demonstrates improved evaluation accuracy through numerical examples

Ensures more comprehensive and fair assessments

Enhances transparency in decision-making processes

Abstract

Modern methods for multi-criteria assessment (MCA), such as Data Envelopment Analysis (DEA), Stochastic Frontier Analysis (SFA), and Multiple Criteria Decision-Making (MCDM), are utilized to appraise a collection of Decision-Making Units (DMUs), also known as alternatives, based on several criteria. These methodologies inherently rely on assumptions and can be influenced by subjective judgment to effectively tackle the complex evaluation challenges in various fields. In real-world scenarios, it is essential to incorporate both quantitative and qualitative criteria as they consist of cardinal and ordinal data. Despite the inherent variability in the criterion values of different alternatives, the homogeneity assumption is often employed, significantly affecting evaluations. To tackle these challenges and determine the most appropriate alternative, we propose a novel MCA approach that combines two Virtual Gap Analysis (VGA) models. The VGA framework, rooted in linear programming, is pivotal in the MCA methodology. This approach improves efficiency and fairness, ensuring that evaluations are both comprehensive and dependable, thus offering a strong and adaptive solution. Two comprehensive numerical examples demonstrate the accuracy and transparency of our proposed method. The goal is to encourage continued advancement and stimulate progress in automated decision systems and decision support systems.