Integrated Flight Scheduling and Fleet Assignment Under Airport Congestion

Abstract

Airport congestion is a major cause for the large delays that currently affect the air transport industry. These delays have huge cost implications—for the U.S. economy these costs were estimated at $32.9 billion in 2007. In this paper, we present a mixed-integer linear optimization model aimed at assisting airlines in the making of integrated flight scheduling and fleet assignment decisions that take aircraft and passenger delay costs explicitly into account. The objective of the model is to maximize the expected profits of an airline that faces a given origin/destination-based travel demand and operates in congested, slot-constrained airports. Both airline competition and airline cooperation are dealt with in the model, though in a simplified manner. The model was applied to a case study involving the main network of TAP Portugal, which comprises 31 airports and 100 daily flight legs. The results obtained through the model suggest that the Portuguese legacy carrier can improve their expected profits significantly, while diminishing the total number of flights and slightly increasing the passengers' average connecting time. The calculation effort involved in the application of the model even on a desktop computer is small enough to allow its real-time utilization in International Air Transport Association scheduling conferences. These findings clearly indicate that the model is a significant addition to the airline planning toolbox.

Integrated Airline Scheduling: Considering Competition Effects and the Entry of the High Speed Rail

“…These enhanced models, however, do not account for changes in passenger demand resulting from competition. Studies that have considered the impact of competition on travel demand include, for example, Hansen (1990); Hong and Harker (1992); Yan, Tang, and Lee (2007); Wei and Hansen (2007) ;Pita, Barnhart, and Antunes (2013); and Vaze and Barnhart (2012a). These studies, however, do not consider multimodal competition and do not differentiate between different types of airlines, i.e., legacy airlines (LAs) and low-cost airlines (LCAs).…”

“…We model recapture for a given market w and passenger type ζ using two different parameters: the split ratio (ϕ w + , ζ w ) and the recapture rate (χ ζ w ). We assume that once passengers are spilled, they are spilled to other available market-time periods (similar to Pita, Barnhart, and Antunes 2013). The split ratio determines the split of passengers from the spilling time period to the time periods before and after it.…”

Smooth and Controlled Recovery Planning of Disruptions in Rapid Transit Networks

“…The airline industry has been a leader in the development of integrated approaches for scheduling and recovering from disruptions. There has been research in the integration of problems such as flight schedule and fleet assignment ( [6], [17]), fleet assignment and aircraft routing ( [20]), aircraft routing and crew scheduling ( [18]), and scheduling and competitive effects ( [8], [21]). All these problems were first developed and solved in a sequential fashion.…”

Improving robustness of rolling stock circulations in rapid transit networks

“…The airline industry has been a leader in the development of integrated approaches for schedule planning and recovering from disruptions. There has been research in the integration of problems such as flight schedule and fleet assignment [20,9,11], fleet assignment and aircraft routing [25], aircraft routing and crew scheduling [24], and scheduling and competition [27,13]. All these problems were first developed and solved in a sequential fashion.…”