DEVELOPMENT OF SOME INTUITIONISTIC FUZZY DEA MODELS AND THEIR APPLICATIONS TO POLICE, BANKING AND EDUCATION SECTORS

Abstract

Data Envelopment Analysis (DEA), introduced by Charnes et al. [54] in 1978, has emerged as a robust analytical and quantitative methodology for measuring the performance of a broad range of similar organizations. Since then, DEA has grown remarkably in various fields. It is a data-oriented approach for evaluating the performance of a set of homogeneous peer entities called Decision-Making Units (DMUs), which utilize multiple inputs to produce multiple outputs. It is a non-parametric method applied to a wide range of govt./non-govt. organizations such as police stations, banks, education institutes, hospitals, transportation, libraries, airlines, etc. In literature, numerous techniques Stochastic frontier analysis (SFA) [5], Distribution free approach (DFA) [42], Thick frontier approach (TFA) [43] and Free disposal hull (FDH) [72] have been developed to assess the efficiency of DMUs. A significant advantage [68] of DEA over other established performance methodologies is that it operates without any initial presumptions regarding the structure of the production frontier. Instead, DEA concentrates solely on attaining a singular measure of production efficiency for each DMU under study. DEA, a widely utilized method, has found application in various sectors. The police, banks, and education sectors serve as significant illustrations of the wide-ranging implementation of DEA. Police Sector: Police stations serve as a critical point of contact between the public and the criminal justice system. They serve as the frontline law enforcement agencies responsible for maintaining public safety and order. Evaluating the efficiency and ranking of police stations are essential factors to consider for research purposes, as they provide valuable insights into the performance and effectiveness of law enforcement agencies. Police stations’ efficiency and ranking can also help identify disparities in law enforcement practices across different regions and communities. It can highlight areas where law enforcement agencies need additional support, resources, and training to improve their performance. In order to comprehend the operational framework of the DEA methodology into real-life problems, data from the Indian police stations have been acquired. Efficiency scores and ranking are presented by considering India’s states and union territories as DMUs.