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The dual dea unit

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For almost any linear plan (LP) it will be possible to produce a partner VINYLSKIVA using the same data, as well as the solution to possibly the original VINYLSKIVA (the primal) or the spouse (the dual) provides the same information about the difficulty being modelled. DEA is no exception to this. The dual model is definitely constructed simply by assigning a variable (dual variable) to each constraint in the primal style and constructing a new model about these factors. This is shown below.

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The first thing to notice is that the primal model offers n + t & m & 1 limitations whilst the dual style has meters + t constraints. As n, the quantity of units, is generally considerably bigger than t & m, the quantity of inputs and outputs, it might be seen which the primal style will have more constraints compared to the dual model. For geradlinig programs in general the more constraints the more hard a problem is always to solve. Therefore for this reason it truly is usual to fix the dual DEA model rather than the primitive.

Through the theory of linear coding it is regarded that the values of the dual variables as a result of solving a dual unit are similar to the darkness prices in the primal style. The dual variables Lambda(j) are thus also the shadow prices related to the constraints limiting the productivity of each device to be zero greater than 1 . It is also well-known that where a constraint can be binding, a shadow value will be confident normally and where the constraint is non-binding the shadow price will be zero. In the solution to the primal style therefore a binding constraint implies that the corresponding unit posseses an efficiency of 1 and you will see a positive darkness price or dual changing. Hence positive shadow rates in the fundamental, or confident values to get the Lambda(j) ‘s inside the dual, correspond to and discover the expert group for just about any inefficient unit.


You should not explicitly designate a statistical form for the production function.

Proven to be useful in unveiling relationships that remain concealed for other methodologies.

Capable of handling multiple inputs and outputs.

Capable of being used with virtually any input-output measurement.

The sources of ineffectiveness can be analysed and quantified for every examined unit.


Answers are sensitive to the selection of advices and outputs (Berg 2010).

You are unable to test to get the best specification (Berg 2010).

The number of efficient firms for the frontier is likely to increase while using number of advices and result variables (Berg 2010).

A prefer to Improve upon DEA, by reducing its disadvantages or strengthening its positive aspects has been a major cause for a large number of discoveries inside the recent books.

The currently usually DEA-based strategy to obtain one of a kind efficiency rankings is called cross-efficiency.

Limitations of Data Envelopment Evaluation

DEA is a great approach but it features its restrictions. You must recognize that DEA is like a dark-colored box. Since the weights that are used in the effectiveness ratio of each and every record are different, trying to make clear how and why every score was calculated can be pointless. Usually we give attention to the ranking of the records rather than on the actual beliefs of the effectiveness scores. Likewise note that the existence of extremums may cause the results to have suprisingly low values.

Have in mind that DEA uses linear combos of the features to calculate the ratios. Thus in the event that combining these people linearly is usually not suitable in our application, we must apply transformations for the features and make them feasible to be linearly combined. One other drawback of this system is that we must solve several linear programming problems because the number of information, something that requires a lot of computational resources. Another problem that DEA encounters is that that work well with high dimensional data.


DEA is a story approach to comparable efficiency dimension where there are multiple incommensurate inputs and outputs. If a suitable set of measures can be defined DEA provides an performance measure not really relying on the use of a common weighting of the inputs and outputs. Additionally the technique identifies peer units and targets pertaining to inefficient units. A number of problems arising from the use of DEA have also been addressed.

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