Virtual Gap Analysis procedures for Multi-Criteria Decision-Making and Efficiency Analysis Problems

TL;DR

This paper introduces a new Virtual Gap Analysis-based efficiency evaluation method that overcomes limitations of traditional DEA and SFA, providing robust, assumption-free performance assessments for decision-making units.

Contribution

It presents a novel, linear programming-based Virtual Gap Analysis approach for efficiency evaluation and integrates it with existing MCDM techniques for improved decision-making.

Findings

VGA models are linear, assumption-free, and robust.

The method accurately classifies DMUs as efficient or inefficient.

Integration reduces effort in selecting optimal alternatives.

Abstract

Existing multi-criteria decision-making (MCDM) methods often face challenges when evaluating a large number of alternatives, leading to skewed results in selecting the optimal choice. Similarly, conventional efficiency analysis (EA) methods, such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), often yield incomplete solutions due to their reliance on theoretical assumptions. To address these limitations, we propose a novel EA method that integrates Virtual Gap Analysis (VGA) models to evaluate the performance of each decision-making unit (DMU) in relation to others based on best practices. Unlike DEA and SFA, our VGA models are linear programming-based, assumption-free, and capable of delivering robust and reliable solutions. The proposed method enables each DMU to identify achievable benchmarks for inputs and outputs. Based on the estimated virtual gaps, DMUs are classified as inefficient (with scores below one) or efficient (with scores of one or higher). Additionally, our new MCDM method incorporates existing MCDM techniques to analyze the few identified efficient DMUs, significantly reducing the effort required to select the best DMU.