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An Airspace Planning Model for Selecting Flight-plans Under Workload, Safety, and Equity Considerations

Author

Listed:
  • Hanif D. Sherali

    (Grado Department of Industrial and Systems Engineering (0118), Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061)

  • J. Cole Smith

    (Department of Systems and Industrial Engineering, University of Arizona, Tucson, Arizona 85721)

  • Antonio A. Trani

    (Charles Edward Via, Jr. Department of Civil and Environmental Engineering (0105), Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061)

Abstract

In this paper, we present an airspace planning model (APM) that has been developed for use in both tactical and strategic planning contexts under various airspace scenarios. Given a set of flights for a particular time horizon, along with (possibly several) alternative flight-plans for each flight that are based on delays and diversions, due to special-use airspace (SUA) restrictions prompted by launches at spaceports or adverse weather conditions, this model prescribes a set of flight-plans to be implemented. The model formulation seeks to minimize and delay fuel-cost-based objective function, subject to the constraints that each flight is assigned one of the designated flight-plans, and that the resulting set of flight-plans satisfies certain specified workload, safety, and equity criteria. These requirements ensure that the workload for air-traffic controllers in each sector is held under a permissible limit, that any potential conflicts are routinely resolvable, and that the various airlines involved derive equitable levels of benefits from the overall implemented schedule. To solve the resulting 0–1 mixed-integer programming problem more effectively using commercial software (e.g., CPLEX-MIP), we explore the use of reformulation techniques designed to more closely approximate the convex hull of feasible solutions to the problem. We also prescribe a polynomial-time heuristic procedure that is demonstrated to provide solutions to the problem within 0.01% of optimality. Computational results are reported on several scenarios based on actual flight data obtained from the Federal Aviation Administration (FAA) to demonstrate the efficacy of the proposed approach for air-traffic management (ATM) purposes.

Suggested Citation

  • Hanif D. Sherali & J. Cole Smith & Antonio A. Trani, 2002. "An Airspace Planning Model for Selecting Flight-plans Under Workload, Safety, and Equity Considerations," Transportation Science, INFORMS, vol. 36(4), pages 378-397, November.
    • Handle: RePEc:inm:ortrsc:v:36:y:2002:i:4:p:378-397
    DOI: 10.1287/trsc.36.4.378.546
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    References listed on IDEAS

    as
    1. 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.
    2. Hanif D. Sherali & Warren P. Adams & Patrick J. Driscoll, 1998. "Exploiting Special Structures in Constructing a Hierarchy of Relaxations for 0-1 Mixed Integer Problems," Operations Research, INFORMS, vol. 46(3), pages 396-405, June.
    3. A. M. Geoffrion, 1969. "An Improved Implicit Enumeration Approach for Integer Programming," Operations Research, INFORMS, vol. 17(3), pages 437-454, June.
    4. Dimitris Bertsimas & Sarah Stock Patterson, 1998. "The Air Traffic Flow Management Problem with Enroute Capacities," Operations Research, INFORMS, vol. 46(3), pages 406-422, June.
    5. Dimitris Bertsimas & Sarah Stock Patterson, 2000. "The Traffic Flow Management Rerouting Problem in Air Traffic Control: A Dynamic Network Flow Approach," Transportation Science, INFORMS, vol. 34(3), pages 239-255, August.
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    Cited by:

    1. Dal Sasso, Veronica & Djeumou Fomeni, Franklin & Lulli, Guglielmo & Zografos, Konstantinos G., 2018. "Incorporating Stakeholders’ priorities and preferences in 4D trajectory optimization," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 594-609.
    2. Samà, Marcella & D’Ariano, Andrea & D’Ariano, Paolo & Pacciarelli, Dario, 2017. "Scheduling models for optimal aircraft traffic control at busy airports: Tardiness, priorities, equity and violations considerations," Omega, Elsevier, vol. 67(C), pages 81-98.
    3. Dal Sasso, Veronica & Djeumou Fomeni, Franklin & Lulli, Guglielmo & Zografos, Konstantinos G., 2019. "Planning efficient 4D trajectories in Air Traffic Flow Management," European Journal of Operational Research, Elsevier, vol. 276(2), pages 676-687.
    4. 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.
    5. 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.
    6. Regnier, Eva, 2008. "Doing something about the weather," Omega, Elsevier, vol. 36(1), pages 22-32, February.
    7. Zhe Liang & Wanpracha Art Chaovalitwongse & Elsayed A. Elsayed, 2014. "Sequence Assignment Model for the Flight Conflict Resolution Problem," Transportation Science, INFORMS, vol. 48(3), pages 334-350, August.
    8. Wei, P. & Cao, Y. & Sun, D., 2013. "Total unimodularity and decomposition method for large-scale air traffic cell transmission model," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 1-16.
    9. Lehouillier, Thibault & Omer, Jérémy & Soumis, François & Desaulniers, Guy, 2017. "Two decomposition algorithms for solving a minimum weight maximum clique model for the air conflict resolution problem," European Journal of Operational Research, Elsevier, vol. 256(3), pages 696-712.
    10. Asteris, Michael & Collins, Alan & Jones, Dylan F., 2012. "Container port infrastructure in north-west Europe: Policy-level modeling," Journal of Policy Modeling, Elsevier, vol. 34(2), pages 312-324.
    11. Sun, D. & Clinet, A. & Bayen, A.M., 2011. "A dual decomposition method for sector capacity constrained traffic flow optimization," Transportation Research Part B: Methodological, Elsevier, vol. 45(6), pages 880-902, July.

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