Measuring transport systems efficiency under uncertainty by fuzzy sets theory based Data Envelopment Analysis: theoretical and practical comparison with traditional DEA model

In transportation management the measure of systems efficiency is a key issue in order to verify the performances and propose the best countermeasure to achieve the prefixed goals. Many efforts have been made in this field to provide satisfactory answer to this problem. One of the most used methodologies is the Data Envelopment Analysis (DEA) that has been in many fields. The DEA technique is a useful is non-parametric method that allow to handle many output and input at the same time. In many real world applications, input and output data cannot be precisely measured. Imprecision (or approximation) and vagueness may be originated from indirect measurements, model estimation, subjective interpretation, and expert judgment or available information from different sources. Therefore, methodologies that allow the analyst to explicitly deal with imprecise or approximate data are of great interest, especially in freight transport where available data as well as stakeholders’ behavior often suffer from vagueness or ambiguity. This is particularly worrying when assessing efficiency with frontier-type models, such as Data Envelopment Analysis (DEA) models, since they are very sensitive to possible imprecision in the data set. In this paper, we have specified a Fuzzy Theory-based DEA model to assess efficiency of transportation systems and services considering uncertainty in data, as well as in the evaluation result. In particular, we have applied the proposed fuzzy DEA model to evaluate the efficiency of a selected set of international container ports. In particular, we focus on the “delay time” that is an important input data that is usually non easy to measure and then is considered as uncertain. Finally, a comparison of ports efficiency obtained by the proposed fuzzy DEA model and traditional DEA has been carried out in order to evaluate the differences between the two methods. © 2014 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of SIDT2013 Scientific Committee.