Integrated Disruption Management and Flight Planning to Trade Off Delays and Fuel Burn

In this paper we present a novel approach addressing airline delays and recovery. Airline schedule recovery involves making decisions during operations to minimize additional operating costs while getting back on schedule as quickly as possible. The mechanisms used include aircraft swaps, flight cancellations, crew swaps, reserve crews, and passenger rebookings. In this context, we introduce another mechanism, namely flight planning that enables flight speed changes. Flight planning is the process of determining flight plan(s) specifying the route of a flight, its speed, and its associated fuel burn. Our key idea in integrating flight planning and disruption management is to adjust the speeds of flights during operations, trading off flying time (and along with it, block time) and fuel burn; in combination with existing mechanisms, such as flight holds. Our goal is striking the right balance of fuel costs and passenger-related delay costs incurred by the airline.We present both exact and approximate models for integrated aircraft and passenger recovery with flight planning. From computational experiments on data provided by a European airline, we estimate that the ability of our approach to control (decrease or increase) flying time by trading off with fuel burn, as well as to hold downstream flights, results in reductions in passenger disruptions by approximately 66%-83%, accompanied by small increases in fuel burn of 0.152%-0.155% and a total cost savings of approximately 5.7%-5.9% for the airline, may be achieved compared to baseline approaches typically used in practice. We discuss the relative benefits of two mechanisms studied-specifically, flight speed changes and intentionally holding flight departures, and show significant synergies in applying these mechanisms. The results, compared with recovery without integrated flight planning, are because of increased swap possibilities during recovery, decreased numbers of flight cancellations, and fewer disruptions to passengers

Mitigation of airspace congestion impact on airline networks

AbstractIn recent years European airspace has become increasingly congested and airlines can now observe that en-route capacity constraints are the fastest growing source of flight delays. In 2010 this source of delay accounted for 19% of all flight delays in Europe and has been increasing with an average yearly rate of 17% from 2005 to 2010. This paper suggests and evaluates an approach to how disruption management can be combined with flight planning in order to create more proactive handling of the kind of disruptions, which are caused by congested airspace. The approach is evaluated using data from a medium size European carrier and estimates a lower bound saving of several million USD

Mitigation of airspace congestion impact on airline networks

In recent years European airspace has become increasingly congested and airlines can now observe that en-route capacity constraints are the fastest growing source of flight delays. In 2010 this source of delay accounted for 19% of all flight delays in Europe and has been increasing with an average yearly rate of 17% from 2005 to 2010. This paper suggests and evaluates an approach to how disruption management can be combined with flight planning in order to create more proactive handling of the kind of disruptions, which are caused by congested airspace. The approach is evaluated using data from a medium size European carrier and estimates a lower bound saving of several million USD

Constraint Programming Based Column Generation for Crew Assignment

Airline crew assignment problems are large-scale optimization problems which can be adequately solved by column generation. The subproblem is typically a so-called constrained shortest path problem and solved by dynamic programming. However, complex airline regulations arising frequently in European airlines cannot be expressed entirely in this framework and limit the use of pure column generation. In this paper, we formulate the subproblem as a constraint satisfaction problem, thus gaining high expressiveness