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Proceedings ArticleDOI

Network DEA efficiency improvement under constant sum of inputs/outputs

TL;DR: Data Envelopment Analysis models and algorithms to reduce inputs and increase outputs for an inefficient Decision Making Unit (DMU) in a network DEA and will help the decision maker to allocate resources more effectively among various divisions in order to improve the efficiency of the system consisting of these interrelated divisions.
Abstract: This paper proposes, Data Envelopment Analysis (DEA) models and algorithms to reduce inputs and increase outputs for an inefficient Decision Making Unit (DMU) in a network DEA. Inputs and outputs considered for change are under constant sum constraint. In a network, DMU composed of multiple divisions, it is possible that changes in inputs and outputs of one division may affect the efficiency of other divisions. At the same time, it is also possible to change the efficiency of a division without changing the efficiency of the entire network. Models to reallocate inputs/outputs under constant sum constraint need to take these possibilities into account. The reallocation of inputs or outputs is done without reducing the network DEA efficiency of the other DMUs. Models and algorithms have been developed to transfer intermediate products from one DMU to another under constant sum constraint. These models and algorithms will help the decision maker to allocate resources more effectively among various divisions in order to improve the efficiency of the system consisting of these interrelated divisions. Theoretical results have been illustrated with the help of a numerical example.
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Journal ArticleDOI
TL;DR: A nonlinear (nonconvex) programming model provides a new definition of efficiency for use in evaluating activities of not-for-profit entities participating in public programs and methods for objectively determining weights by reference to the observational data for the multiple outputs and multiple inputs that characterize such programs.

25,433 citations

Journal ArticleDOI
TL;DR: The CCR ratio form introduced by Charnes, Cooper and Rhodes, as part of their Data Envelopment Analysis approach, comprehends both technical and scale inefficiencies via the optimal value of the ratio form, as obtained directly from the data without requiring a priori specification of weights and/or explicit delineation of assumed functional forms of relations between inputs and outputs as mentioned in this paper.
Abstract: In management contexts, mathematical programming is usually used to evaluate a collection of possible alternative courses of action en route to selecting one which is best. In this capacity, mathematical programming serves as a planning aid to management. Data Envelopment Analysis reverses this role and employs mathematical programming to obtain ex post facto evaluations of the relative efficiency of management accomplishments, however they may have been planned or executed. Mathematical programming is thereby extended for use as a tool for control and evaluation of past accomplishments as well as a tool to aid in planning future activities. The CCR ratio form introduced by Charnes, Cooper and Rhodes, as part of their Data Envelopment Analysis approach, comprehends both technical and scale inefficiencies via the optimal value of the ratio form, as obtained directly from the data without requiring a priori specification of weights and/or explicit delineation of assumed functional forms of relations between inputs and outputs. A separation into technical and scale efficiencies is accomplished by the methods developed in this paper without altering the latter conditions for use of DEA directly on observational data. Technical inefficiencies are identified with failures to achieve best possible output levels and/or usage of excessive amounts of inputs. Methods for identifying and correcting the magnitudes of these inefficiencies, as supplied in prior work, are illustrated. In the present paper, a new separate variable is introduced which makes it possible to determine whether operations were conducted in regions of increasing, constant or decreasing returns to scale in multiple input and multiple output situations. The results are discussed and related not only to classical single output economics but also to more modern versions of economics which are identified with "contestable market theories."

14,941 citations

Journal ArticleDOI
01 May 1957

14,922 citations

Journal ArticleDOI
TL;DR: A slacks-based network DEA model is proposed, called Network SBM, that can deal with intermediate products formally and evaluate divisional efficiencies along with the overall efficiency of decision making units (DMUs).

954 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a dynamic DEA model involving network structure in each period within the framework of a slacks-based measure approach, and applied this model to a dataset of US electric utilities and compared the result with that of DSBM.
Abstract: We propose a dynamic DEA model involving network structure in each period within the framework of a slacks-based measure approach. We have previously published the network SBM (NSBM) and the dynamic SBM (DSBM) models separately. Hence, this article is a composite of these two models. Vertically, we deal with multiple divisions connected by links of network structure within each period and, horizontally, we combine the network structure by means of carry-over activities between two succeeding periods. This model can evaluate (1) the overall efficiency over the entire observed period, (2) dynamic change of period efficiency and (3) dynamic change of divisional efficiency. The model can be implemented in input-, output- or non-(both) oriented forms under the CRS or VRS assumptions on the production possibility set. Finally, we applied this model to a dataset of US electric utilities and compared the result with that of DSBM.

480 citations