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Documents authored by Atkin, Jason A. D.

Found 2 Possible Name Variants:

Atkin, Jason A. D.

Document
DOI: 10.4230/OASIcs.ATMOS.2011.134
On the Utilisation of Fuzzy Rule-Based Systems for Taxi Time Estimations at Airports

Authors: Jun Chen, Stefan Ravizza, Jason A. D. Atkin, and Paul Stewart

Published in: OASIcs, Volume 20, 11th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (2011)

Abstract
The primary objective of this paper is to introduce Fuzzy Rule-Based Systems (FRBSs) as a relatively new technology into airport transportation research, with a special emphasis on ground movement operations. Hence, a Mamdani FRBS with the capability to learn from data has been adopted for taxi time estimations at Zurich Airport (ZRH). Linear regression is currently the dominating technique for such an estimation task due to its established nature, proven mathematical characteristics and straightforward explanatory ability. In this study, we demonstrate that FRBSs, although having a more complex structure, can offer more accurate estimations due to their proven properties as nonlinear universal approximators. Furthermore, such improvements in accuracy do not come at the cost of the model's interpretability. FRBSs can offer more explanations of the underlying behavior in different regions. Preliminary results on data for ZRH suggest that FRBSs are a valuable alternative to already established linear regression methods. FRBSs have great potential to be further seamlessly integrated into the taxiway routing and scheduling process due to the fact that more information is now available in the explanatory variable space.
Cite as

Jun Chen, Stefan Ravizza, Jason A. D. Atkin, and Paul Stewart. On the Utilisation of Fuzzy Rule-Based Systems for Taxi Time Estimations at Airports. In 11th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems. Open Access Series in Informatics (OASIcs), Volume 20, pp. 134-145, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@InProceedings{chen_et_al:OASIcs.ATMOS.2011.134,
  author =	{Chen, Jun and Ravizza, Stefan and Atkin, Jason A. D. and Stewart, Paul},
  title =	{{On the Utilisation of Fuzzy Rule-Based Systems for Taxi Time Estimations at Airports}},
  booktitle =	{11th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems},
  pages =	{134--145},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-33-0},
  ISSN =	{2190-6807},
  year =	{2011},
  volume =	{20},
  editor =	{Caprara, Alberto and Kontogiannis, Spyros},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2011.134},
  URN =		{urn:nbn:de:0030-drops-32736},
  doi =		{10.4230/OASIcs.ATMOS.2011.134},
  annote =	{Keywords: Fuzzy rule-based system, Taxi time estimation, Airport ground movement}
}

Atkin, Jason

Document
DOI: 10.4230/OASIcs.ATMOS.2012.132
Probabilistic Airline Reserve Crew Scheduling Model

Authors: Christopher Bayliss, Geert De Maere, Jason Atkin, and Marc Paelinck

Published in: OASIcs, Volume 25, 12th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (2012)

Abstract
This paper introduces a probabilistic model for airline reserve crew scheduling. The model can be applied to any schedules which consist of a stream of departures from a single airport. We assume that reserve crew demand can be captured by an independent probability of crew absence for each departure. The aim of our model is to assign some fixed number of available reserve crew in such a way that the overall probability of crew unavailability in an uncertain operating environment is minimised. A comparison of different probabilistic objective functions, in terms of the most desirable simulation results, is carried out, complete with an interpretation of the results. A sample of heuristic solution methods are then tested and compared to the optimal solutions on a set of problem instances, based on the best objective function found. The current model can be applied in the early planning phase of reserve crew scheduling, when very little information is known about crew absence related disruptions. The main conclusions include the finding that the probabilistic objective function approach gives solutions whose objective values correlate strongly with the results that these solutions will get on average in repeated simulations. Minimisation of the sum of the probabilities of crew unavailability was observed to be the best surrogate objective function for reserve crew schedules that perform well in simulation. A list of extensions that could be made to the model is then provided, followed by conclusions that summarise the findings and important results obtained.
Cite as

Christopher Bayliss, Geert De Maere, Jason Atkin, and Marc Paelinck. Probabilistic Airline Reserve Crew Scheduling Model. In 12th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems. Open Access Series in Informatics (OASIcs), Volume 25, pp. 132-143, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InProceedings{bayliss_et_al:OASIcs.ATMOS.2012.132,
  author =	{Bayliss, Christopher and De Maere, Geert and Atkin, Jason and Paelinck, Marc},
  title =	{{Probabilistic Airline Reserve Crew Scheduling Model}},
  booktitle =	{12th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems},
  pages =	{132--143},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-45-3},
  ISSN =	{2190-6807},
  year =	{2012},
  volume =	{25},
  editor =	{Delling, Daniel and Liberti, Leo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2012.132},
  URN =		{urn:nbn:de:0030-drops-37091},
  doi =		{10.4230/OASIcs.ATMOS.2012.132},
  annote =	{Keywords: airline reserve, crew scheduling, probabilistic model}
}
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