data envelopment analysis (dea) is a method to measure relative efficiency of a set of decision-making units (dmus) which uses multiple inputs and produces multiple outputs. in the conventional dea, crisp inputs and outputs are fundamentally necessary. but the observed values of inputs and outputs in real-world problems are sometimes imprecise. thus, performance measurement often needs to be do...

The changing economic conditions have challenged many financial institutions to search for more efficient and effective ways to assess emerging markets. Data Envelopment Analysis (DEA) is a widely used mathematical programming technique that compares the inputs and outputs of a set of homogenous Decision Making Units (DMUs) by evaluating their relative efficiency. In the conventional DEA model,...

As a useful management and decision tool, data envelopment analysis (DEA) has become a pop area of research. One of the topics of interests in DEA is sensitivity and stability analysis of decision making units (DMUs). Due to the uncertainty of the data in real life, this paper will give some DEA models in fuzzy environment. It is followed by a series analysis of sensitivity and stability for al...

Data envelopment analysis (DEA) is a linear programming based non-parametric technique for evaluating the relative efficiency of homogeneous decision making units (DMUs) on the basis of multiple inputs and multiple outputs. There exist radial and non-radial models in DEA. Radial models only deal with proportional changes of inputs/outputs and neglect the input/output slacks. On the other hand, ...

This paper deals with the problem of efficiency assessment using Data Envelopment Analysis (DEA) when input and output data are given as fuzzy sets. In particular, a extension measure inefficiency proportions, well-known slacks-based additive measure, is considered. The proposed approach also provides targets. Computational experiences comparison other DEA approaches reported.

In this paper a model for fuzzy SBM of efficiency in DEA is presented based on a-cuts of the fuzzy number parameters to convert fuzzy linear programming problem to an interval programming problem. Then, by introducing a variable in each of the obtained intervals and applying suitable substitutions, a linear programming problem would be achieved. To demonstrate the concept, numerical examples ar...

Traditional DEA models do not deal with imprecise data and assume that the data for all inputs and outputs are known exactly. Inverse DEA models can be used to estimate inputs for a DMU when some or all outputs and efficiency level of this DMU are increased or preserved. this paper studies the inverse DEA for fuzzy data. This paper proposes generalized inverse DEA in fuzzy data envelopment anal...

A business organization’s objective is to make better decisions at all levels of the firm to improve performance. Typically organizations are multi-faceted and complex systems that use uncertain information. Therefore, making quality decisions to improve organizational performance is a daunting task. Organizations use decision support systems that apply different business intelligence technique...

Flexible Manufacturing System (FMS) offers opportunities for manufacturers to improve their technology, competitiveness, and profitability through a highly efficient and focused approach to manufacturing effectiveness. Data envelopment analysis (DEA) has been utilized as a multiple criteria tool for evaluation of FMSs. The concept of the assurance region (AR) is restricting the ratio of any two...