Abstract
The work is devoted to the development of new methods and algorithms to support decision making when planning air travel using uncertainties in the form of fuzzy numbers. The proposed approach makes it possible to define rational methods of choice: how to change the transport graph to better meet the needs of the population. This is particularly relevant in the context of the reduced demand for air travel caused by the pandemic and the need to switch from large to smaller aircraft types. The problem is solved by restoring the fuzzy origin–destination matrix of current statistics on air traffic between airports. The problem is that we do not know what proportion of passengers moving between the specified points are forced to use large transport hubs as intermediate destinations. To determine the validity of the origin–destination matrix, we build a number of optimization models to determine fuzzy intervals and search for correspondence with the maximum value of the membership function. Algorithmic and software search for the fuzzy origin–destination matrix and fuzzy ranking of potentially promising routes are developed. The perspective of the given approach is shown by an example of a task concerning a choice of new routes between regional airports.
Highlights
Development of intelligent approaches to strategic planning of transport flows and operational decision support systems in logistics fulfils the goal of taking a leading position in the development and use of air and space, the World Ocean, the Arctic and Antarctic
It is necessary to find an origin–destination matrix between these represented airports: how many people have moved between the given target start and target end
This paper solves the problem of finding the fuzzy OD matrix
Summary
Development of intelligent approaches to strategic planning of transport flows and operational decision support systems in logistics fulfils the goal of taking a leading position in the development and use of air and space, the World Ocean, the Arctic and Antarctic. The modeling of transport flows and the evaluation of modeling results is complicated by a significant proportion of uncertain factors. These include pandemics, fundamental changes in the economy, and unpredictable political and military changes. Probabilistic estimates of these uncertainties are not always possible due to the uniqueness of situations and the lack of necessary statistical data
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