IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v263y2018i1d10.1007_s10479-017-2747-1.html
   My bibliography  Save this article

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

Author

Listed:
  • Huiyuan Fan

    (Johnson Controls)

  • Prashant K. Tarun

    (Missouri Western State University)

  • Victoria C. P. Chen

    (University of Texas at Arlington)

  • Dachuan T. Shih

    (PFS, Tenet)

  • Jay M. Rosenberger

    (University of Texas at Arlington)

  • Seoung Bum Kim

    (Korea University)

  • Robert A. Horton

    (Dallas Fort Worth International Airport)

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.

Suggested Citation

  • Huiyuan Fan & Prashant K. Tarun & Victoria C. P. Chen & Dachuan T. Shih & Jay M. Rosenberger & Seoung Bum Kim & Robert A. Horton, 2018. "Data-driven optimization for Dallas Fort Worth International Airport deicing activities," Annals of Operations Research, Springer, vol. 263(1), pages 361-384, April.
    • Handle: RePEc:spr:annopr:v:263:y:2018:i:1:d:10.1007_s10479-017-2747-1
    DOI: 10.1007/s10479-017-2747-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-017-2747-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-017-2747-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Julia Tsai & Victoria Chen & M. Beck & Jining Chen, 2004. "Stochastic Dynamic Programming Formulation for a Wastewater Treatment Decision-Making Framework," Annals of Operations Research, Springer, vol. 132(1), pages 207-221, November.
    2. Victoria C. P. Chen & David Ruppert & Christine A. Shoemaker, 1999. "Applying Experimental Design and Regression Splines to High-Dimensional Continuous-State Stochastic Dynamic Programming," Operations Research, INFORMS, vol. 47(1), pages 38-53, February.
    3. Cervellera, Cristiano & Chen, Victoria C.P. & Wen, Aihong, 2006. "Optimization of a large-scale water reservoir network by stochastic dynamic programming with efficient state space discretization," European Journal of Operational Research, Elsevier, vol. 171(3), pages 1139-1151, June.
    4. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
    5. Zehua Yang & Victoria C. P. Chen & Michael E. Chang & Melanie L. Sattler & Aihong Wen, 2009. "A Decision-Making Framework for Ozone Pollution Control," Operations Research, INFORMS, vol. 57(2), pages 484-498, April.
    6. Chen, Victoria C. P., 1999. "Application of orthogonal arrays and MARS to inventory forecasting stochastic dynamic programs," Computational Statistics & Data Analysis, Elsevier, vol. 30(3), pages 317-341, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yong-Wu Zhou & Chuanying Chen & Yuanguang Zhong & Bin Cao, 2020. "The allocation optimization of promotion budget and traffic volume for an online flash-sales platform," Annals of Operations Research, Springer, vol. 291(1), pages 1183-1207, August.
    2. Anna Maria Sulej-Suchomska & Danuta Szumińska & Miguel de la Guardia & Piotr Przybyłowski & Żaneta Polkowska, 2024. "Airport Runoff Water: State-of-the-Art and Future Perspectives," Sustainability, MDPI, vol. 16(18), pages 1-22, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ariyajunya, Bancha & Chen, Ying & Chen, Victoria C.P. & Kim, Seoung Bum & Rosenberger, Jay, 2021. "Addressing state space multicollinearity in solving an ozone pollution dynamic control problem," European Journal of Operational Research, Elsevier, vol. 289(2), pages 683-695.
    2. Dachuan Shih & Seoung Kim & Victoria Chen & Jay Rosenberger & Venkata Pilla, 2014. "Efficient computer experiment-based optimization through variable selection," Annals of Operations Research, Springer, vol. 216(1), pages 287-305, May.
    3. Zehua Yang & Victoria C. P. Chen & Michael E. Chang & Melanie L. Sattler & Aihong Wen, 2009. "A Decision-Making Framework for Ozone Pollution Control," Operations Research, INFORMS, vol. 57(2), pages 484-498, April.
    4. Cervellera, Cristiano & Chen, Victoria C.P. & Wen, Aihong, 2006. "Optimization of a large-scale water reservoir network by stochastic dynamic programming with efficient state space discretization," European Journal of Operational Research, Elsevier, vol. 171(3), pages 1139-1151, June.
    5. Elcin Koc & Cem Iyigun, 2014. "Restructuring forward step of MARS algorithm using a new knot selection procedure based on a mapping approach," Journal of Global Optimization, Springer, vol. 60(1), pages 79-102, September.
    6. Cervellera, Cristiano, 2023. "Optimized ensemble value function approximation for dynamic programming," European Journal of Operational Research, Elsevier, vol. 309(2), pages 719-730.
    7. Cervellera, C. & Macciò, D., 2011. "A comparison of global and semi-local approximation in T-stage stochastic optimization," European Journal of Operational Research, Elsevier, vol. 208(2), pages 109-118, January.
    8. Banai, Reza, 2010. "Evaluation of land use-transportation systems with the Analytic Network Process," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(1), pages 85-112.
    9. Pishchulov, Grigory & Trautrims, Alexander & Chesney, Thomas & Gold, Stefan & Schwab, Leila, 2019. "The Voting Analytic Hierarchy Process revisited: A revised method with application to sustainable supplier selection," International Journal of Production Economics, Elsevier, vol. 211(C), pages 166-179.
    10. Seung-Jin Han & Won-Jae Lee & So-Hee Kim & Sang-Hoon Yoon & Hyunwoong Pyun, 2022. "Assessing Expected Long-term Benefits for the Olympic Games: Delphi-AHP Approach from Korean Olympic Experts," SAGE Open, , vol. 12(4), pages 21582440221, December.
    11. Seyed Rakhshan & Ali Kamyad & Sohrab Effati, 2015. "Ranking decision-making units by using combination of analytical hierarchical process method and Tchebycheff model in data envelopment analysis," Annals of Operations Research, Springer, vol. 226(1), pages 505-525, March.
    12. V. Srinivasan & G. Shainesh & Anand K. Sharma, 2015. "An approach to prioritize customer-based, cost-effective service enhancements," The Service Industries Journal, Taylor & Francis Journals, vol. 35(14), pages 747-762, October.
    13. Mónica García-Melón & Blanca Pérez-Gladish & Tomás Gómez-Navarro & Paz Mendez-Rodriguez, 2016. "Assessing mutual funds’ corporate social responsibility: a multistakeholder-AHP based methodology," Annals of Operations Research, Springer, vol. 244(2), pages 475-503, September.
    14. Luis Pérez-Domínguez & Luis Alberto Rodríguez-Picón & Alejandro Alvarado-Iniesta & David Luviano Cruz & Zeshui Xu, 2018. "MOORA under Pythagorean Fuzzy Set for Multiple Criteria Decision Making," Complexity, Hindawi, vol. 2018, pages 1-10, April.
    15. Paul L. G. Vlek & Asia Khamzina & Hossein Azadi & Anik Bhaduri & Luna Bharati & Ademola Braimoh & Christopher Martius & Terry Sunderland & Fatemeh Taheri, 2017. "Trade-Offs in Multi-Purpose Land Use under Land Degradation," Sustainability, MDPI, vol. 9(12), pages 1-19, November.
    16. Kumar B, Pradeep, 2021. "Changing Objectives of Firms and Managerial Preferences: A Review of Models in Microeconomics," MPRA Paper 106967, University Library of Munich, Germany, revised 13 Mar 2021.
    17. Greco, Salvatore & Ishizaka, Alessio & Tasiou, Menelaos & Torrisi, Gianpiero, 2018. "σ-µ efficiency analysis: A new methodology for evaluating units through composite indices," MPRA Paper 83569, University Library of Munich, Germany.
    18. Anirban Mukhopadhyay & Sugata Hazra & Debasish Mitra & C. Hutton & Abhra Chanda & Sandip Mukherjee, 2016. "Characterizing the multi-risk with respect to plausible natural hazards in the Balasore coast, Odisha, India: a multi-criteria analysis (MCA) appraisal," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1495-1513, February.
    19. Chamoli, Sunil, 2015. "Hybrid FAHP (fuzzy analytical hierarchy process)-FTOPSIS (fuzzy technique for order preference by similarity of an ideal solution) approach for performance evaluation of the V down perforated baffle r," Energy, Elsevier, vol. 84(C), pages 432-442.
    20. H. S. C. Perera & W. K. R. Costa, 2008. "Analytic Hierarchy Process for Selection of Erp Software for Manufacturing Companies," Vision, , vol. 12(4), pages 1-11, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:263:y:2018:i:1:d:10.1007_s10479-017-2747-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.