In this article, I propose a hybrid data envelopment analysis (DEA) system that utilizes a methodology combining the tier analysis with the neural clustering method. I aim to show that the hybrid system can be used to evaluate the inter-organizational efficiency in the life insurance companies. The application is unfolded in two phases. In the first phase, DEA is repetitively used to evaluate t...

Data envelopment analysis (DEA) is an axiomatic, mathematical programming approach to productive efficiency analysis and performance measurement. This paper shows that DEA can be interpreted as a nonparametric least squares regression subject to shape constraints on production frontier and sign constraints on residuals. Thus, DEA can be seen as a nonparametric counter-part of the corrected ordi...

This article computes the energy productivity changes of regions in Japan using total-factor frameworks based on data envelopment analysis (DEA). Since the traditional DEA-Malmquist index cannot analyze changes in single-factor productivity changes under the total-factor framework, we apply a new index proposed by Hu and Chang [2009. Total-factor energy productivity growth of regions in China. ...

This paper develops and demonstrates a quality control framework for bus schedule reliability. Automatic vehicle location (AVL) devices provide necessary data; data envelopment analysis (DEA) yields a valid summary measure from partial reliability indicators; and panel data analysis provides statistical confidence boundaries for each route-direction’s DEA scores. If a route-direction’s most rec...

data envelopment analysis (dea) is a method to evaluate the relative efficiency of decision making units (dmus). in this method, the issue has always been to determine a set of weights for each dmu which often caused many problems. since the dea models also have the multi-objective linear programming (molp) problems nature, a rational relationship can be established between molp and dea problem...

this paper proposes a new approach for determining efficient dmus in dea models using inverse optimi-zation and without solving any lps. it is shown that how a two-phase algorithm can be applied to detect effi-cient dmus. it is important to compare computational performance of solving the simultaneous linear equa-tions with that of the lp, when computational issues and complexity analysis are a...

This paper addresses comprehensive ranking systems determining an ordering of entities by aggregating quantitative data for multiple attributes. We propose a DEA-CP (Data Envelopment Analysis Compromise Programming) model for the comprehensive ranking, including preference voting (ranked voting) to rank candidates in terms of aggregate vote by rank for each candidate. Although the DEA-CP model ...

Data envelopment analysis (DEA) models have been used in formulating the Malmquist index to measure productivity change over time periods. In this article, we question the validity of this index in the presence of input/output slacks that appear in DEA models used. We demonstrate with an application to banking that Malmquist index loses its meaning whenever slacks are present. As a corrective m...

Some specific geometric data envelopment analysis (DEA) models are well known to the researchers in DEA through so-called multiplicative or log-linear efficiency models. Valuable properties of these models were noted by several authors but the models still remain somewhat obscure and rarely used in practice. The purpose of this paper is to show from a mathematical perspective where the geometri...

Aggregating measure scores is an important step in multiple criteria decision-making, determining the overall effectiveness from dissimilar measures. One of the most popular aggregating methods is Simple Additive Weighting (SAW), which assigns weights to measures based on subjective preference information, and sum weighted scores up. The quality of overall score is highly dependent on the quali...