data envelopment analysis (dea) and balanced scorecard (bsc) are two well-known approaches for measuring performance of decision making units (dmus). bsc is especially applied with quality measures, whereas, when the quantity measures are used to evaluate, dea is more appropriate. in the real-world, dmus usually have complex structures such as network structures. one of the well-known network s...

Clinicians and hospital administrators rely on information models for healthcare management and decision-making. However, healthcare surgical performance measurements include both qualitative and quantitative data, often with conflicting and interdependent variables. As a result many statistical modeling approaches can break down with healthcare data. Other more resilient algorithms, such as Da...

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. ...

When organizations do not have well defined goals and constraints, traditional mixed integer programming MIP models are ineffective for portfolio selection. In such cases, some organizations revert to building project portfolios based on data envelopment analysis DEA relative efficiency scores. However, implementing the k most efficient projects until resources are expended will not always resu...

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...

Data envelopment analysis (DEA) is used here to determine the relative eciency of 24 top ranked US MBA programs. Eciency scores were determined using three output sets of the MBA programs: output that measured student satisfaction, output that measured recruiter satisfaction and output that measured both. DEA is also used here to determine the relative eciency of three foreign MBA programs a...