Passenger-Centric Slot Allocation at Schedule-Coordinated Airports

Sebastian Birolini (), Alexandre Jacquillat (), Phillip Schmedeman () and Nuno Ribeiro ()
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Sebastian Birolini: Department of Management, Information and Production Engineering, University of Bergamo, 24123 Bergamo, Italy
Alexandre Jacquillat: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142
Phillip Schmedeman: Department of Systems Engineering, United States Military Academy, West Point, New York 10996
Nuno Ribeiro: Aviation Studies Institute, Engineering Systems and Design, Singapore University of Technology and Design, Singapore 487372

Transportation Science, 2023, vol. 57, issue 1, 4-26

Abstract: Schedule coordination is the primary form of demand management used at busy airports. At its core, slot allocation involves a highly complex combinatorial problem. In response, optimization models have been developed to minimize the displacement of flight schedules from airline requests, subject to physical and administrative constraints. Existing approaches, however, may not result in the best itineraries for passengers. This paper proposes an original passenger-centric approach to airport slot allocation to maximize available itineraries and minimize connecting times. Because of the uncertainty regarding passenger demand, the proposed approach combines predictive analytics to forecast passenger flows in flight networks from historical data and prescriptive analytics to optimize airport slot assignments in view of flight-centric and passenger-centric considerations. The problem is formulated as a mixed-integer nonconvex optimization model. To solve it, we propose an approximation scheme that alternates between flight-scheduling and passenger-accommodation modules and embed it into a large-scale neighborhood search algorithm. Using real-world data from the Singapore Changi and Lisbon Airports, we show that the proposed model and algorithm return solutions in acceptable computational times. Results suggest that slot-allocation outcomes can be made much more consistent with passenger flows at a relatively small cost in terms of flight displacement. Ultimately, this paper provides a new paradigm that can create more attractive flight schedules by bringing together airport-level considerations, airline-level considerations, and, for the first time, passenger-level considerations.

Keywords: slot allocation; multiobjective optimization; integer programming; analytics (search for similar items in EconPapers)
Date: 2023
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