Envelopment Analysis (DEA) is a very eective method to evaluate the relative eciency of decision-making units (DMUs). DEA models divided all DMUs in two categories: ecient and inecientDMUs, and don't able to discriminant between ecient DMUs. On the other hand, the observedvalues of the input and output data in real-life problems are sometimes imprecise or vague, suchas interval data, ordinal da...

In many real world applications (even in banking), imprecise data is a matter of fact. However, classic database management systems provide little if any help in the management of imprecise data. We are applying methods from interval arithmetic, epsilon serializability, and other related areas to help the application designers in the management of naturally imprecise data. Our approach includes...

In many real world applications (even in banking), imprecise data is a matter of fact. However, classic database management systems provide little if any help in the management of imprecise data. We are applying methods from interval arithmetic, epsilon serializability, and other related areas to help the application designers in the management of naturally imprecise data. Our approach includes...

Data envelopment analysis (DEA) is a method for measuring the relative efficiencies of a set of decision-making units (DMUs) that use multiple inputs to produce multiple outputs. In this paper, we study the measurement of DMU performances in DEA in situations where input and/or output values are given as imprecise data. By imprecise data we mean situations where we only know that the actual val...

Nowadays, with respect to knowledge growth about enterprise sustainability, sustainable supplier selection is considered a vital factor in sustainable supply chain management. On the other hand, usually in real problems, the data are imprecise. One method that is helpful for the evaluation and selection of the sustainable supplier and has the ability to use a variety of data types is data envel...