Classic data envelopment analysis (DEA) models determine the efficiency of productive units, called decision making units (DMUs). DEA uses as its methodology the equiproportional reduction of inputs or increase of outputs and the finding of a single target for each DMU. This target does not incorporate the preference of the decision maker. Later works propose obtaining alternative targets based...

Data envelopment analysis (DEA) is widely used as a benchmarking tool for improving productive performance of decision making units (DMUs). The benchmarks produced by DEA are obtained as a sideproduct of computing efficiency scores. As a result, the benchmark units may differ from the evaluated DMU in terms of their input–output profiles and the scale size. Moreover, the DEA benchmarks may oper...

This paper proposes a new efficiency benchmarking methodology that is capable of incorporating probability while still preserving the advantages of a distribution-free and nonparametric modeling technique. This new technique developed in this paper will be known as the DEA-Chebyshev model. The foundation of DEA-Chebyshev model is based on the model pioneered by Charnes, Cooper, and Rhodes in 19...

Nowadays, data envelopment analysis (DEA) is a well-established non-parametric methodology for performance evaluation and benchmarking. DEA has witnessed a widespread use in many application areas since the publication of the seminal paper by Charnes, Cooper and Rhodes in 1978. However, to the best of our knowledge, no published work formally addressed out-of-sample evaluation in DEA. In this p...

Multiple target tracking (MTT) is taken into account as one of the most important topics in tracking targets with radars. In this paper, the MTT problem is used for estimating the position of multiple targets when a 2-D radar is employed to gather measurements. To do so, the Joint Probabilistic Data Association Filter (JPDAF) approach is applied to tracking the position of multiple targets. To ...

Our context of interest is tree-structured exact search in metric spaces. We make the simple observation that, the deeper a data item is within the tree, the higher the probability of that item being excluded from a search. Assuming a fixed and independent probability p of any subtree being excluded at query time, the probability of an individual data item being accessed is (1− p) for a node at...

In today’s economy and society, performance analyses in the services industries attract more and more attention. The traditional data envelopment analysis (DEA) approach requires a consistent operating environment. However, in reality, there is a need to evaluate the units belonging to different environment. This reality challenges the traditional methods of applying DEA theory to real-world ca...