A TRIANGULAR FUZZY DEA MODEL FOR EFFICIENCY EVALUATION

DEA models for supply chain efficiency evaluation

An appropriate performance measurement system is an important requirement for the effective management of a supply chain. Two hurdles are present in measuring the performance of a supply chain and its members. One is the existence of multiple measures that characterize the performance of chain members, and for which data must be acquired; the other is the existence of conflicts between the memb...

Secondary Model Developed for Weight Selective in Evaluating the Efficiency of Cross-DEA with Fuzzy Data

Fuzzy Efficiency Measures in DEA: A New Approach based on Fuzzy DEA Approach with Double Frontiers

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...

Evaluating Supplier Performance Using DEA and Piecewise Triangular Fuzzy AHP

Data envelopment analysis (DEA) has been widely applied in evaluating multicriteria decision making problems, which have multi-input and multi-output. However, the traditional DEA method does neither take the decision maker’s subjective preferences to the individual criteria into consideration nor rank the selected options or decision making units (DMUs). On the other hand, Satty’s analytical h...

Different T-norms for Dea-fuzzy Efficiency Computations

Interval DEA frontiers are here used in situations where one input or output is subject to uncertainty in its measurement and is presented as an interval data. We built an efficient frontier without any assumption about the probability disttribution function of the imprecise variable. We take into account only the minimum and the maximum values of each imprecise variable. Two frontiers are cons...