The variable returns to scale data envelopment analysis (DEA) model is developed with a maintained hypothesis of convexity in input-output space. This hypothesis is not consistent with standard microeconomic production theory that posits an S-shape for the production frontier, i.e. for production technologies that obey the Regular Ultra Passum Law. Consequently, measures of technical efficiency...

Many types of facility location/allocation models have been developed to find optimal spatial patterns with respect to location criteria such as: cost, time, coverage, and access. In this paper we develop and test location modeling formulations that utilize aspects of the data envelopment analysis (DEA) efficiency measure to find optimal and efficient facility location/allocation patterns. Solv...

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...

Benchmarking cleaner production performance is an effective way of pollution control and emission reduction in coal-fired power industry. A benchmarking method using two-stage super-efficiency data envelopment analysis for coal-fired power plants is proposed – firstly, to improve the cleaner production performance of DEA-inefficient or weakly DEA-efficient plants, then to select the benchmark f...

Within the data envelopment analysis context, problems of discrimination between efficient and inefficient decision-making units often arise, particularly if there are a relatively large number of variables with respect to observations. This paper applies Monte-Carlo simulation to generalize and compare two discrimination-improving methods; principal component analysis applied to data envelopme...

of Workshop 2015 -Advances in DEA Theory and Applications with Extensions to Forecasting Models Date December 1 st -2 nd , 2015 Venue: Conference Room 4A, National Graduate Institute for Policy Studies (GRIPS) Workshop Organizer: Prof. Kaoru Tone, GRIPS December 1 st , 13:25-17:00 13:25-13:30 Opening Remarks: Prof. Kaoru Tone (GRIPS, Japan) 13:30-15:00 Session 1. Chair: Prof. Joseph Paradi (Uni...

data envelopment analysis (dea) is a managerial powerful tool for assess the performance of decision making units (dmus). nowadays, multi-objective dea models are an attractive technique for evaluation quantity and quality aspects of performance analysis. but these models are in static. in this paper, we want to present a proposed fuzzy dynamic multi-objective dea model in which data are changi...

on account of the existence of uncertainty, dea occasionally faces the situation of imprecise data, especially when a set of dmus include missing data, ordinal data, interval data, stochastic data, or fuzzy data. therefore, how to evaluate the efficiency of a set of dmus in interval environments is a problem worth studying. in this paper, we discussed the new method for evaluation and ranking i...

The purpose of this paper is to analyze the optimal scale of local jurisdictions (cantons) in Switzerland applying Data Envelopment Analysis (DEA) to the years 2000 to 2004. Aggregate output performance indicators for four local government activities (administration, education, health, and transportation) are used to measure technical and scale efficiency and to derive DEA scores. Results show ...

Data envelopment analysis (DEA) has been a wildly used powerful method to measure efficiencies of decision making units (DMUs). However, DEA efficiency scores are influenced by uncontrollable factors for respective DMUs. Previous studies attempted separating such factors from DEA scores. Fried et al. [4] proposed a multi-stage data adjustment approach using DEA and a regression model, and sever...