Assessment of airside aerodrome infrastructure by SAW method with weights from Shannon's interval entropy
DOI:
https://doi.org/10.5604/01.3001.0015.6929Keywords:
entropy, aerodrome infrastructure, SAWAbstract
Multi-criteria decision support (MCDM) methods are widely used in many areas of science. This applies to economic, social and technical sciences. Implementing activities at the strategic, tactical or operational level requires appropriate tools to support decision-makers. The use of these tools requires the preparation of a decision model along with the formalization of the goal and the acquisition and preparation of data to make the decision accurate. Due to the wide application of MCDM in engineering practice, the article presents their application in air transport. It is an area that is constantly evolving, and all decisions at the strategic level have long-term effects and must be adequately justified. In the paper a compartmental extension of the classical SAW method with weights obtained using the compartmental Shannon entropy was proposed. This paper presents issues concerning the choice of airport layout and describes the problems that occur in determining the cost and capacity of airports. This paper reviews the literature on airport capacity and operations and airside air transport processes and the application of various multi-criteria decision support methods to airport problems. The main part of the article contains an optimization mathematical model aimed at determining the parameters of the elements comprising the airport, on the basis of which a simulation model was developed and a modified method of multi-criteria evaluation of SAW taking into account the interval numbers was presented, in which the set of weights was estimated by the Shannon entropy method. In the application part for 3 variants of the airport arrangement, the parameters were determined in the form of interval numbers and then evaluated using the presented method. The presented numerical example shows that the proposed method is an excellent tool to assist in solving complex decision problems where the data are imprecise and represented by interval numbers.
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