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Park and De [35] developed an alternative approach to efficiency measurement of seaports using DEA, which they refer to as a "Four-Stage DEA Method": i) productivity; ii) profitability; iii) marketability, and iv) overall efficiency.
However, the DEA method does not evaluate the shift in the frontier over time; instead DEA only estimates the performance of DMUs in reference to the best practice frontier in a given year.
The classic DEA method, CCR, is used in this paper; in the future, some other DEA methods, such as BCC, can be used, and the performance of each method can be compared.
Additionally, Hung et al. [21] used the bootstrapped DEA method in order to reduce the statistical noise (mainly, due to sampling bias) of the basic DEA models.
We formulate the noise count rate and show the advantage of DEA method quantitatively.
The standard DEA method requires that the status of all input and output variables be known exactly.
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Written by experts, who are generally major contributors to the topics to be covered, it includes a comprehensive review and discussion of basic DEA models, which, in the present issue extensions to the basic DEA methods, and a collection of DEA applications in the areas of banking, engineering, health care, and services.
One of the existing DEA methods' limitations noted in the literature lies in the process of benchmarking of reference targets for inefficient DMUs.
Traditional DEA methods treat a production unit as a black box without any consideration to the internal relations of its sub-processes.
Therefore, it is able to rank all extreme efficient DMUs in the DEA methods with constraint and variable Returns to Scale and so, eliminate the existing difficulties in some methods.
Malmquist DEA methods allow for calculation of change in efficiency across two or more time periods.
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