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Modelling Operational Delays at Airports

Research output: Contribution to conference - Without ISBN/ISSN Poster

Published
Publication date20/04/2017
Original languageEnglish
Event1st Joint IMA/ORS Conference - Aston University, Birmingham, United Kingdom
Duration: 20/04/201721/04/2017
https://ima.org.uk/1365/ima-society-conference-mathematics-operational-research/

Conference

Conference1st Joint IMA/ORS Conference
CountryUnited Kingdom
CityBirmingham
Period20/04/1721/04/17
Internet address

Abstract

The question of how to reduce the frequency and severity of flight delays at airports has become increasingly topical in recent years due to the relentless increase in air traffic growth which has placed the resources of many of the world’s busiest airports under seemingly unsustainable pressure. Since the expansion of an airport’s infrastructure is usually not realistic in the short-term, it appears that there is no “quick fix” to the problem. Instead, airports must find ways of adapting to the increasing demand by improving the efficiency of their operations.

The OR-MASTER project, led by researchers at Lancaster University, is concerned with optimising the allocation of scarce airport resources. An essential part of this involves the modelling of operational (queueing) delays which are stochastic in nature. Given a schedule of expected arrivals and departures at an airport, the methods of queueing theory can be employed to analyse the delays that are likely to occur and assess whether or not the airport is expected to meet key performance targets. This type of analysis can be used to inform the decision-making process which determines the airport’s declared capacity.

Given that thousands of aircraft traverse a vast global network of airports on a daily basis, a further objective of the project is to study how delays which occur at individual airports propagate around the network. Algorithms for the analysis of queueing delays at a network level must incorporate novel methods for approximating the probability distributions of complicated, non-stationary queueing systems in a time-efficient manner if they are to be useful in practice.