Data envelopment analysis (DEA) is a method to evaluate the efficiency of some decision making units which by using one or more inputs will make one or more outputs. In real world, most of the problems don’t have a certain mode. Fuzzy theory is one of the ways of considering uncertainty in the mathematical programming problems. In this study by using this idea, the DEA model on a fully fuzzy mo...

Data envelopment analysis (DEA) is a widely used mathematical programming approach for evaluating the relative efficiency of decision making units (DMUs) in organizations. Crisp input and output data are fundamentally indispensable in traditional DEA evaluation process. However, the input and output data in real-world problems are often imprecise or ambiguous. In this study, we present a four-p...

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...

Data Envelopment Analysis (DEA) is a nonparametric approach for measuring the relative efficiency of a decision making units consists of multiple inputs and outputs. In all standard DEA models semi positive real valued measures are assumed, while in some real cases inputs and outputs may take complex valued. The question is related to measuring efficiency in such cases. As far as we are aware, ...

Abstract The shortest path problem (SPP) is a special network structured linear programming that appears in wide range of applications. Classical SPPs consider only one objective the networks while some or all multiple, conflicting and incommensurate objectives such as optimization cost, profit, time, distance, risk, quality service may arise together real-world These types are known multi-obje...