Energy Efficiency Analysis of Decision Making Units Using MC-ERM-DEA with Imprecise Data

Data envelopment analysis (DEA), is a mathematical technique to evaluate the performance of various organization in public and private sector. The standard DEA method requires that the values for all inputs and outputs are known exactly, when some inputs and outputs are imprecise data, such as interval or bounded data, the resulting DEA model becomes a non-linear programming problem. Such a DEA model is called imprecise DEA (IDEA) in the literature. measure .Referred to as an Enhanced Russell Measure (ERM) , the resulting model is in the form of a fractional program. Hence, it can be transformed into an ordinary linear programming structure that can generate an optimal solution for the corresponding ERM model. In this paper we consider efficiency aggregation with enhanced Russell measure in DEA while are interval parameters (IDEA). With the economic development, the clean and low-carbon integrated energy system is of great significance to the social sustainable development in the world. Therefore, this paper proposes a novel energy supply efficiency evaluation model of integrated energy systems based on Enhanced-Russell measure data envelopment analysis (ERM-DEA) integrating Monte Carlo for energy saving and optimization.