Development of fully intuitionistic fuzzy data envelopment analysis model with missing data: an application to Indian police sector

TL;DR

This paper introduces a novel fully intuitionistic fuzzy Data Envelopment Analysis model that effectively handles missing and uncertain data, demonstrated through an application to Indian police stations' efficiency measurement.

Contribution

It proposes a new fully intuitionistic fuzzy input minimization BCC model to address missing and uncertain data in DEA, with validation on real-world police sector data.

Findings

Successfully measures police station efficiencies despite data uncertainties.

Demonstrates robustness of the model in real-life application.

Enhances accuracy of efficiency analysis with fuzzy data handling.

Abstract

Data Envelopment Analysis (DEA) is a technique used to measure the efficiency of decision-making units (DMUs). In order to measure the efficiency of DMUs, the essential requirement is input-output data. Data is usually collected by humans, machines, or both. Due to human/machine errors, there are chances of having some missing values or inaccuracy, such as vagueness/uncertainty/hesitation in the collected data. In this situation, it will be difficult to measure the efficiencies of DMUs accurately. To overcome these shortcomings, a method is presented that can deal with missing values and inaccuracy in the data. To measure the performance efficiencies of DMUs, an input minimization BCC (IMBCC) model in a fully intuitionistic fuzzy (IF) environment is proposed. To validate the efficacy of the proposed fully intuitionistic fuzzy input minimization BCC (FIFIMBCC) model and the technique to deal with missing values in the data, a real-life application to measure the performance efficiencies of Indian police stations is presented.