Data envelopment analysis modeling in a two- level network for performance measurement and prediction under uncertainty conditions using the game theory and data mining approach

Abstract

Calculating and analyzing performance in organizations is of great importance. In this research, we aim to present a new approach in studies related to two-tier networks for evaluating and predicting cost efficiency under fuzzy uncertainty conditions in various provinces of Iran. Classic models have been combined with game theory, fuzzy theory, and data mining methods for this purpose. This combined model was used to evaluate the efficiency of provincial insurance and medical centers of the Social Security Organization of the country. As a result, the ranking of provincial units of the Social Security Organization of the country was determined based on their insurance and medical status, and smaller provinces such as Zanjan, Hamedan, and Qazvin had a higher level of efficiency than larger provinces such as East Azerbaijan, Kerman, and Mazandaran. The final innovation of this research can be considered the use of a more comprehensive combined model based on a fuzzy data envelopment analysis model with slack-based measure and neural network data mining model to evaluate the efficiency of provincial insurance and medical centers of the Social Security Organization of the country.