Abstract In contrast to classical data envelopment analysis (DEA), network DEA has attention the internal structure of a production system and reveals relationship between efficiency efficiencies processes. However, flexibility weights need for crisp input output in evaluation process are two major shortcomings models. This paper presents common approach relational model fuzzy environment measu...

background and objectives: evaluating the performance of clinical units is critical for effective managementof health settings. certain assessment of clinical variables for performance analysis is not always possible,calling for use of uncertainty theory. this study aimed to develop and evaluate an integrated independentcomponent analysis-fuzzy-data envelopment analysis approach to accurate the...

Data envelopment analysis (DEA) is a non-parametric technique for measuring and analyzing the relative efficiency of Decision-Making Unit (DMU) having multiple homogeneous types inputs outputs. Traditional DEA treats DMU as black boxes calculates their efficiencies by considering initial final But overall directly depended upon performances intermediate processes operations. In many situations,...

In the today’s world wherein every routine is based on economic factors, there is no doubt that theoretical sciences are driven by their capabilities and affordances in terms of economy. As a mathematical tool, data envelopment analysis (DEA) is provided to economics, so that one can investigate associated costs, prices and revenues of economic units. Data Envelopment Analysis (DEA) is a linear...

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...