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. The standard DEA models assume that all inputs and outputs are crisp and can be changed at the discretion of management. While crisp input and output data are fundamentally indispensable in the standard DEA evalua...

   Recently, Farzipoor Saen [Journal of the Operational Research Society, 60(11), 1575–1582 (2009)] proposed a method based on data envelopment analysis to identify optimistic efficient suppliers in the presence of nondiscretionary factors-imprecise data. This short communication aims at showing a computational error in computing the value of preference intensity parameter in Farzipoor Saen’s [...

In this work, a regression problem is studied where the elements of the database are sets with certain geometrical properties. In particular, our model can be applied to handle data affected by some kind of noise or uncertainty and interval-valued data, and databases with missing values as well. The proposed formulation is based on the standard -Support Vector Regression approach. In the interv...

Trajectory queries, which retrieve nearby objects for every point of a given route, can be used to identify alerts of potential threats along a vessel route, or monitor the adjacent rescuers to a travel path. However, the locations of these objects (e.g., threats, succours) may not be precisely obtained due to hardware limitations of measuring devices, as well as the constantly-changing nature ...

This paper provides a supervised classification tree-based methodology to deal with Multivalued data, specifically predictors measurements can be provided by a functional distribution or an interval of values. Main literature refers to symbolic data analysis, aiming to extend standard methods such as factorial analysis, clustering, discriminant analysis, etc., to deal with symbolic data tables....

The Theory of Belief Functions is a formal framework for reasoning with uncertainty that is well suited for representing unreliable information and weak states of knowledge. In information fusion applications, it is mainly used in a centralized way, by gathering the data on a single node before computation. In this paper, a distributed algorithm is proposed to compute the neighborhood confidenc...

Uncertainties are widely present in the design and simulation of aero-engine combustion systems. Common non-probabilistic convex models only capable processing independent or correlated uncertainty variables, while conventional precise probabilistic sensitivity analysis based on ideal conditions also fails due to presence uncertainties. Given above-described problem, an imprecise p-box method i...

Recently, various deterministic inventory models were developed for deteriorating items with the uniform demand pattern (either increasing or decreasing) throughout cycle. However, such types of are not suitable many real business problems. In particular, patterns steady models, ordinarily, rises first, then it becomes static and finally decreases, demands can be portrayed by considering trapez...