Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. In the conventional DEA, all the data assume the form of specific numerical values. However, the observed values...

In this work, a discrete Proportional-Integral-Derivative (PID) controller was trained using a Tagaki-Sugeno type fuzzy logic system. The PID parameters were acquired online without the need for manual tuning, calibration or prior knowledge of plant parameters. The developed system has the...

This paper proposes a clustering algorithm, Fuzzy CLARA, which combines Fuzzy C-Medoids algorithm (FCMDD) with Clustering LARge Applications (CLARA) algorithm with an application of the proposed algorithm for fast image segmentation. CLARA finds wide applications in different areas of data...

Performance evaluation in conventional data envelopment analysis (DEA) requires crisp numerical values. However, the observed values of the input and output data in real-world problems are often imprecise or vague. These imprecise and vague data can be represented by linguistic terms...

In this paper, by introducing two virtual decision making units (DMUs) called ideal DMU (IDMU) and anti-ideal DMU (ADMU) with fuzzy inputs-outputs, the efficiency evaluates of DMUs are done by fuzzy data envelopment analysis (FDEA). Therefore, we evaluate DMUs from the perspective of the best...

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...

In a recent paper, Jiménez et al. [M. Jiménez, M. Arenas, A. Bilbao, M.V. Rodríguez. Linear programming with fuzzy parameters: An interactive method resolution. European Journal of Operational Research 177(2007) 1599-1609] propose a “general” and “interactive” method for solving...

Although crisp data are fundamentally indispensable for determining the profit Malmquist productivity index (MPI), the observed values in the real-world problems are often imprecise or vague. These imprecise or vague data can be suitably characterized with fuzzy and interval methods. In this...