This paper presents a statistical methodology for analyzing a complex phenomenon in which deterministic and scaling components are superimposed. 10 Our approach is based on the wavelet multiresolution analysis combined with the scaling analysis of the entropy of a time series. The wavelet multiresolution analysis decomposes the signal in a scale-by-scale manner. The scale-by-scale decomposition...

Data envelopment analysis (DEA) is a non-parametric method for measuring the efficiency of a set of decision making units (DMUs), such as companies or public sector agencies. The main DEA models are only used for positive data. In recent years, some models have been presented to deal with negative data in DEA models. However, these models do not discriminate between efficient DMUs and only eval...

Data envelopment analysis (DEA) is a non-parametric approach to evaluate a set of decision making units (DMUs) consuming multiple inputs to produce multiple outputs. Formally, DEA use to estimate the efficiency score into the empirical efficient frontier. Also, DEA can be used to allocate resources and set targets for future forecast. The data are continuous in the standard DEA model whereas th...

Some indexes are necessary for performance evaluation of a management system. Balanced scorecard (BSC) is used in order to extraction of these indexes for evaluating the necessary performances in the evaluation process. Utilization of BSC causes to prevent in increasing information and data. Furthermore, all of the important indexes are considered in evaluation performance.Data envelopment anal...

OBJECTIVES This study examined how ethanolamines (EAs) with the same functional alcohol group (HOCH(2)CH(2)), such as mono-EA (MEA), di-EA (DEA), and tri-EA (TEA), in water-based metalworking fluids (wbMWFs) are vaporized, condensed, and transformed by heat generated during metalworking. METHODS Two types of experimental apparatus were manufactured to achieve these objectives. RESULTS Vapor...

Data envelopment analysis (DEA) measures relative efficiency among the decision making units (DMU) without considering noise in data. The least efficient DMU indicates that it is in the worst situation. In this paper, we measure efficiency of individual DMU whenever it losses the maximum output, and the efficiency of other DMUs is measured in the observed situation. This efficiency is the minim...

this paper introduces discretionary imprecise data in data envelopment analysis (dea) and discusses the efficiency evaluation of decision making units (dmus) with non-discretionary imprecise data. then, suggests a method for evaluation the efficiency of dmus with non-discretionary imprecise data. when some inputs and outputs are imprecise and non-discretionary, the dea model becomes non-linear ...

Traditional DEA method is improper for supplier evaluation and selection, as it adopts varying weights in evaluation, and fails to consider competition among the suppliers. In order to solve these two problems, Nash bargaining game DEA model is applied to supplier evaluation in present paper. However, there is a non-uniqueness problem with Nash bargaining game efficiency of supplier in existing...

Abstract: The radial measures of classical DEA models (CCR, BCC) are incomplete, they are only separate measures of input and output efficiency and their efficiency index omit the non-zero input and output slacks. Enhanced Russell graph measure (ERM) eliminates these deficiencies. All of the existing fuzzy DEA models are extension of CCR or BCC model, efficiencies of DMUs, ultimately, are solut...

Lack of information about technology and prices often hampers the empirical assessment of the validity of the profit maximization hypothesis. We show that the non-parametric Data Envelopment Analysis (DEA) methodology comprises natural tools for dealing with such incomplete information. In particular, we focus on the economic meaning of the DEA model that builds on assumptions of monotone and c...