Introduction: Data envelopment analysis (DEA) is a data-oriented method for measuring and benchmarking the relative efficiency of peer decision making units (DMUs) with multiple inputs and multiple outputs. DEA was initiated in 1978 when Charnes, Cooper and Rhodes (CCR) demonstrated how to change a fractional linear measure of efficiency into a linear programming format. This non-parametric app...

A deficiência múltipla (DMu) vem sendo tratada como um quadro complexo de definição e caracterização. Esse tipo tem sido pouco estudado no Brasil, principalmente âmbito educacional. Os objetivos deste estudo foram descrever analisar as definições dos termos DMu sensorial (DMS) em teses dissertações brasileiras. Realizou-se uma revisão sistemática na base dados da Biblioteca Digital Brasileira T...

Tracking the influence of service improvements on passengers particularly is an important component transport management. This study examined characteristics intercity travellers in Economy Class Units (ECU) and recently introduced Diesel Multiple Unit (DMU) within Lagos District Nigeria Railway Corporation south-western Nigeria. A structured questionnaire was randomly administered to 1206 trav...

One of the important issues in data envelopment analysis (DEA) is sensitivity analysis. Heretofore the existent studies have considered the data modification of inputs and outputs in one or multiple DMUs. In this paper the number of DMUs is increased and a stability region is obtained in  by applying defining hyperplanes in which if the added DMU (only one DMU) is in this region then all of the...

The Spherical Frontier DEA Model (SFM) (Avellar et al., 2007) was developed to be used when one wants to fairly distribute a new and fixed input to a group of Decision Making Units (DMU’s). SFM’s basic idea is to distribute this new and fixed input in such a way that every DMU will be placed on an efficiency frontier with a spherical shape. We use SFM to analyze the problems that appear when on...

The paper introduces stochastic DEA with a Perfect Object (SDEA PO). The Perfect Object (PO) is a virtual Decision Making Unit (DMU) that has the smallest inputs and greatest outputs. Including the PO in a collection of actual objects yields an explicit formula of the efficiency index. Given the distributions of DEA inputs and outputs, this formula allows us to derive the probability distributi...