Fairness and Collaboration in Network Air Traffic Flow Management: An Optimization Approach

Dimitris Bertsimas () and Shubham Gupta ()
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Dimitris Bertsimas: Sloan School of Management and Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Shubham Gupta: Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139

Transportation Science, 2016, vol. 50, issue 1, 57-76

Abstract: Air traffic flow management (ATFM) attempts to maintain a safe and efficient flow of aircraft given demand-capacity mismatches while ensuring an equitable distribution of delays among stakeholders. There has been extensive research addressing network effects (such as the presence of multiple airports, sectors, and connectivity requirements) in ATFM, but it has not explicitly incorporated the equitable distribution of delays, as well as work on the equitable distribution of delays in a single-airport setting, such as ration-by-schedule (RBS) as introduced under the collaborative decision-making paradigm. In this paper, we develop a two stage approach for network ATFM that incorporates fairness and airline collaboration. In Stage 1, we propose a discrete optimization model that attempts to incorporate an equitable distribution of delays among airlines by introducing a notion of fairness in network ATFM models—controlling the number of reversals and total amount of overtaking, which is a natural generalization of RBS. For two flights f and f ′, a reversal occurs when flight f ′ arrives before f , when f was scheduled to arrive before f ′. In the event a reversal occurs, the number of time periods between the arrival times constitutes overtaking. In Stage 2, we allow for airline collaboration by proposing a network model for slot reallocation. We provide extensive empirical results of the proposed optimization models on national-scale, real-world data sets spanning six days that show interesting trade-offs between fairness and efficiency. We report computational times of less than 30 minutes for up to 25 airports and provide theoretical evidence that illuminates the strength of our approach.

Keywords: air traffic flow management; collaborative decision-making; discrete optimization; fairness in allocation (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (11)

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