Data Envelopment Analysis (DEA) is a relatively new “data oriented” non-parametric approach for evaluating the performance of complex entities called Decision Making Units (DMUs) which convert multiple inputs into multiple outputs. DEA as a linear programming procedure computes a comparative ratio of outputs to inputs for each DMU, which is reported as the relative efficiency score. In a relati...

In this study, Data Envelopment Analysis (DEA) and Principal Component Analysis (PCA) were compared when these two methods are used for ranking Decision Making Units (DMU) with multiple inputs and outputs. DEA, a nonstatistical technique, is a methodology using a linear programming model for evaluating and ranking DMU’s performance. PCA, a multivariate statistical method, uses new measures defi...

We classify the contributions of DEA literature assessing Decision Making Units (DMUs) whose internal structure is known. Starting from an elementary framework, we define the main research areas as shared flow, multilevel and network models, depending on the assumptions they are subject to. For each model category, the principal mathematical formulations are introduced along with their main var...

In this paper, we extend the standard data envelopment analysis (DEA) model to include longer term top management goals. This extension is in recognition of the fact that benchmarking for decision making units (DMUs) is more than a purely monitoring process, and includes a component of future planning. The new model uses a goal programming structure to find points on the efficient frontier whic...

Many production systems have acquisition and merge operations to increase productivity. This paper proposes a novel method to anticipate whether a merger in a market is generating a major or a minor consolidation, using Inverse data envelopment analysis (InvDEA) model. A merger between two or more decision making units (DMUs) producing a single merged DMU that affects the efficiency frontier, d...

|||||||||||||||||||||||||||||||Abstract One important issue in DEA which has been studied by many DEA researchers is the sensitivity analysis of a speci c DMUo, the unit under evaluation. Moreover, we know that in most models of DEA, the best DMUs have the e ciency score of unity. In some realistic situations, the performance of some ine cient DMUs is similar to that of e cient ones. In this pa...

Abstract—There exists some time lag between the consumption of inputs and the production of outputs. This time lag effect should be considered in calculating efficiency of decision making units (DMU). Recently, a couple of DEA models were developed for considering time lag effect in efficiency evaluation of research activities. However, these models can’t discriminate efficient DMUs because of ...

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative technical efficiency for each member of a set of peer decision making units (DMUs) with multiple inputs and multiple outputs. The original DEA models use positive input and output variables that are measured on a ratio scale, but these models do not apply to the variables in which interval scale data can appe...

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 widely used mathematical programming approach for evaluating the relative efficiency of Decision Making Units (DMUs). Conventional DEA methods treat DMUs as ‘‘black boxes’’, focusing entirely on their relative efficiencies. We propose an efficient two-stage fuzzy DEA model to calculate the efficiency scores for a DMU and its sub-DMUs. We use the Stackelberg ...