Nazila Aghayi

  • Citations Per Year
Learn More
Data envelopment analysis (DEA) is a data-driven non-parametric approach for measuring the efficiency of a set of decision making units (DMUs) using multiple inputs to generate multiple outputs. Conventionally, DEA is used in ex post evaluation of actual performance, estimating an empirical best-practice frontier using minimal assumptions about the shape of(More)
Although crisp data are fundamentally indispensable for determining the profit Malmquist productivity index (MPI), the observed values in real-world problems are often imprecise or vague. These imprecise or vague data can be suitably characterized with fuzzy and interval methods. In this paper, we reformulate the conventional profit MPI problem as an(More)
  • Nazila Aghayi
  • 2016 7th International Conference on Intelligent…
  • 2016
Most DEA models were initially considered only for crisp desirable inputs and outputs, but in the real world data have imprecise value, fuzzy concept is too important as imprecise and undesirable data. Moreover, the decision making units may have been some inputs and outputs index such undesirable data. Therefore, in this study, we introduce a method to(More)
  • 1