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Annals of Operations Research

, Volume 263, Issue 1–2, pp 361–384 | Cite as

Data-driven optimization for Dallas Fort Worth International Airport deicing activities

  • Huiyuan Fan
  • Prashant K. Tarun
  • Victoria C. P. Chen
  • Dachuan T. Shih
  • Jay M. Rosenberger
  • Seoung Bum Kim
  • Robert A. Horton
Data Mining and Analytics
  • 119 Downloads

Abstract

Airplane deicing is a safety measure to eliminate/prevent icing on airplanes that can lead to airflow disruption and emergency conditions. Aircraft deicing/anti-icing fluids (ADF) are high in glycol content. At Dallas Fort Worth International Airport (DFW), the major aircraft deicing activities are conducted at designated deicing pads called Source Isolation Deicing Sites, where ADF run-off can be captured and conveyed into the airport’s glycol collection system. A portion of ADF drips from the aircraft during taxiing and shears off the aircraft during take-off, entering nearby waterways without treatment. Glycol acts as a nutrient for bacteria in the airport’s receiving waterways, resulting in an increase in bacterial growth and a subsequent reduction in dissolved oxygen (DO), potentially endangering aquatic life. This paper proposes a prototype data-driven deicing activities management framework for DFW to address the complexity of airport deicing operations and its impacts. The proposed framework uses stochastic dynamic programming (SDP) to assign airplanes in each hour to deicing pad locations, so as to maximize DO in the receiving waters, subject to airport constraints. Some data were artificially generated using the available knowledge of airport operations. The state transition equations in SDP were estimated. The proposed framework was demonstrated using three cases during major deicing events. Improvements in DO compared with actual DO recorded in the data were mixed; however, the results motivated DFW to implement a new data collection process to replace the artificially-generated data, so that a more accurate optimization could be conducted in the future.

Keywords

Data-driven optimization Stochastic dynamic programming Airport deicing Water quality Glycol Dissolved oxygen 

Notes

Acknowledgements

This research was supported by the Dallas Fort Worth International Airport and the National Science Foundation (ECCS-0801802). The authors would also like to acknowledge Ms. Kerry Stuewer for constructing and testing the Differential Evolution algorithm code.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Huiyuan Fan
    • 1
  • Prashant K. Tarun
    • 2
  • Victoria C. P. Chen
    • 3
  • Dachuan T. Shih
    • 4
  • Jay M. Rosenberger
    • 3
  • Seoung Bum Kim
    • 5
  • Robert A. Horton
    • 6
  1. 1.Johnson ControlsYorkUSA
  2. 2.Steven L. Craig School of BusinessMissouri Western State UniversitySt. JosephUSA
  3. 3.Department of Industrial, Manufacturing, and Systems EngineeringUniversity of Texas at ArlingtonArlingtonUSA
  4. 4.PFS, TenetDallasUSA
  5. 5.Department of Industrial Management EngineeringKorea UniversitySeoulKorea
  6. 6.Dallas Fort Worth International AirportDFW AirportUSA

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