Development of fully intuitionistic fuzzy data envelopment analysis model with missing data: an application to Indian police sector
This is an incremental improvement for researchers and practitioners in data envelopment analysis, specifically in handling fuzzy and missing data in efficiency measurement.
The paper tackled the problem of measuring efficiency in decision-making units when data has missing values and inaccuracies, by proposing a fully intuitionistic fuzzy input minimization BCC model, and applied it to Indian police stations to demonstrate its efficacy.
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.