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Configuration of airspace sectors for balancing air traffic controller workload

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  • Hanif Sherali
  • Justin Hill

Abstract

In this paper, we consider the problem of reconfiguring a section of the national airspace into appropriate sectors from the viewpoint of balancing the predicted air traffic controller workload. The given section of the airspace is specified as a convex polygon in two-dimensions (or a union of such structures), and contains a discretized set of weighted grid points representing localized sub-regions, where the weights reflect the associated air traffic controller monitoring and conflict resolution workloads. We describe four variants of a mixed-integer programming-based algorithmic approach to recursively partition the specified airspace region so as to balance the total weight distribution within each resulting sector. In addition, we augment the proposed model to further accommodate inter-sector coordination workload within this partitioning process, which accounts for the number of flight hand-offs between adjacent sectors. Some illustrative examples are presented to assess the proposed methodology and to investigate the relative computational efficiency and the quality of solutions produced by each algorithmic variant. One competitive procedure is then used to configure a region of airspace over the U.S. using realistic flight data. The main purpose of this work is to provide some modeling concepts and insights to complement the rich body of existing literature on this topic. Copyright Springer Science+Business Media, LLC 2013

Suggested Citation

  • Hanif Sherali & Justin Hill, 2013. "Configuration of airspace sectors for balancing air traffic controller workload," Annals of Operations Research, Springer, vol. 203(1), pages 3-31, March.
    • Handle: RePEc:spr:annopr:v:203:y:2013:i:1:p:3-31:10.1007/s10479-011-0837-z
    DOI: 10.1007/s10479-011-0837-z
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    References listed on IDEAS

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    1. Hanif D. Sherali & Raymond W. Staats & Antonio A. Trani, 2006. "An Airspace-Planning and Collaborative Decision-Making Model: Part II---Cost Model, Data Considerations, and Computations," Transportation Science, INFORMS, vol. 40(2), pages 147-164, May.
    2. Hanif D. Sherali & J. Cole Smith & Antonio A. Trani & Srinivas Sale, 2000. "National Airspace Sector Occupancy and Conflict Analysis Models for Evaluating Scenarios under the Free-Flight Paradigm," Transportation Science, INFORMS, vol. 34(4), pages 321-336, November.
    3. Hanif D. Sherali & Raymond W. Staats & Antonio A. Trani, 2003. "An Airspace Planning and Collaborative Decision-Making Model: Part I—Probabilistic Conflicts, Workload, and Equity Considerations," Transportation Science, INFORMS, vol. 37(4), pages 434-456, November.
    4. Paul Rubin, 1997. "Solving mixed integer classification problems by decomposition," Annals of Operations Research, Springer, vol. 74(0), pages 51-64, November.
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    Cited by:

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    2. Sadeque Hamdan & Oualid Jouini & Ali Cheaitou & Zied Jemai & Tobias Andersson Granberg, 2023. "On the binary formulation of air traffic flow management problems," Annals of Operations Research, Springer, vol. 321(1), pages 267-279, February.
    3. Prot, D. & Rapine, C. & Constans, S. & Fondacci, R., 2014. "A 4D-sequencing approach for air traffic management," European Journal of Operational Research, Elsevier, vol. 237(2), pages 411-425.

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