Stochastic dynamic programming for noise load management

T.R. Meerburg, Richard Boucherie, M.J.A.L. van Kraaij

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

Noise load reduction is among the primary performance targets for some airports. For airports with a complex lay-out of runways, runway selection may then be carried out via a preference list, an ordered set of runway combinations such that the higher on the list a runway combination, the better this combination is for reducing noise load. The highest safe runway combination in the list will actually be used. The optimal preference list selection minimises the probability of exceeding the noise load limit at the end of the aviation year. This paper formulates the preference list selection problem in the framework of Stochastic Dynamic Programming that enables determining an optimal strategy for the monthly preference list selection problem taking into account future and unpredictable weather conditions, as well as safety and efficiency restrictions. The resulting SDP has a finite horizon (aviation year), continuous state space (accumulated noise load), time-inhomogeneous transition densities (monthly weather conditions) and one-step rewards zero. For numerical evaluation of the optimal strategy, we have discretised the state space. In addition, to reduce the size of the state space we have lumped into a single state those states that lie outside a cone of states that may achieve the noise load restrictions. Our results indicate that the SDP approach allows for optimal preference list selection taking into account uncertain weather conditions.
Original languageEnglish
Title of host publicationMarkov Decision Processes in Practice
EditorsRichardus J. Boucherie, Nico van Dijk
Place of PublicationCham
PublisherSpringer
Pages321-335
Number of pages15
ISBN (Print)978-3-319-47764-0
DOIs
Publication statusPublished - 2017

Publication series

NameInternational Series in Operations Research & Management Science
PublisherSpringer International Publishing
Volume248
ISSN (Print)0884-8289

Fingerprint

Dynamic programming
Airports
Aviation
Load limits
Cones

Keywords

  • airport
  • EWI-27919
  • Noise load management
  • Runway preference list selection
  • Stochastic dynamic programming

Cite this

Meerburg, T. R., Boucherie, R., & van Kraaij, M. J. A. L. (2017). Stochastic dynamic programming for noise load management. In R. J. Boucherie, & N. van Dijk (Eds.), Markov Decision Processes in Practice (pp. 321-335). (International Series in Operations Research & Management Science; Vol. 248). Cham: Springer. https://doi.org/10.1007/978-3-319-47766-4_11
Meerburg, T.R. ; Boucherie, Richard ; van Kraaij, M.J.A.L. / Stochastic dynamic programming for noise load management. Markov Decision Processes in Practice. editor / Richardus J. Boucherie ; Nico van Dijk. Cham : Springer, 2017. pp. 321-335 (International Series in Operations Research & Management Science).
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Meerburg, TR, Boucherie, R & van Kraaij, MJAL 2017, Stochastic dynamic programming for noise load management. in RJ Boucherie & N van Dijk (eds), Markov Decision Processes in Practice. International Series in Operations Research & Management Science, vol. 248, Springer, Cham, pp. 321-335. https://doi.org/10.1007/978-3-319-47766-4_11

Stochastic dynamic programming for noise load management. / Meerburg, T.R.; Boucherie, Richard; van Kraaij, M.J.A.L.

Markov Decision Processes in Practice. ed. / Richardus J. Boucherie; Nico van Dijk. Cham : Springer, 2017. p. 321-335 (International Series in Operations Research & Management Science; Vol. 248).

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

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AB - Noise load reduction is among the primary performance targets for some airports. For airports with a complex lay-out of runways, runway selection may then be carried out via a preference list, an ordered set of runway combinations such that the higher on the list a runway combination, the better this combination is for reducing noise load. The highest safe runway combination in the list will actually be used. The optimal preference list selection minimises the probability of exceeding the noise load limit at the end of the aviation year. This paper formulates the preference list selection problem in the framework of Stochastic Dynamic Programming that enables determining an optimal strategy for the monthly preference list selection problem taking into account future and unpredictable weather conditions, as well as safety and efficiency restrictions. The resulting SDP has a finite horizon (aviation year), continuous state space (accumulated noise load), time-inhomogeneous transition densities (monthly weather conditions) and one-step rewards zero. For numerical evaluation of the optimal strategy, we have discretised the state space. In addition, to reduce the size of the state space we have lumped into a single state those states that lie outside a cone of states that may achieve the noise load restrictions. Our results indicate that the SDP approach allows for optimal preference list selection taking into account uncertain weather conditions.

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Meerburg TR, Boucherie R, van Kraaij MJAL. Stochastic dynamic programming for noise load management. In Boucherie RJ, van Dijk N, editors, Markov Decision Processes in Practice. Cham: Springer. 2017. p. 321-335. (International Series in Operations Research & Management Science). https://doi.org/10.1007/978-3-319-47766-4_11