In models of data envelopment analysis (DEA), an optimal set of weights is generally assumed to represent the assessed decision making unit (DMU) in the best light in comparison to all the other DMUs, and so there is an optimal set of weights corresponding to each DMU. The present paper, proposes a three stage method to determine one common set of weights for decision making units. Then, we use...

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 an effective method to evaluate the relative efficiency of decision-making units (DMUs). In one hand, the DEA models need accurate inputs and outputs data. On the other hand, in many situations, inputs and outputs are volatile and complex so that they are difficult to measure in an accurate way. The conflict leads to the researches of uncertain DEA models. Thi...

Abstract Mutual fund (MF) is one of the applicable and popular tools in investment market. The aim this paper to propose an approach for performance evaluation mutual by considering internal structure financial data uncertainty. To reach goal, robust network envelopment analysis (RNDEA) presented extended two-stage structure. In RNDEA method, leader–follower (non-cooperative game) optimization ...

We propose piecewise linear kernel-based support vector clustering (SVC) as a new approach tailored to data-driven robust optimization. By solving a quadratic program, the distributional geometry of massive uncertain data can be effectively captured as a compact convex uncertainty set, which considerably reduces conservatism of robust optimization problems. The induced robust counterpart proble...

The paper is devoted to the problem of generating artificial datasets for data envelopment analysis (DEA), which can be used testing DEA models and methods. In particular, papers that applied big often synthetic generation obtain large-scale because real large size, available in public domain, are extremely rare. This proposes algorithm takes as input some dataset complements it by efficient in...