Airline Scheduling Research Articles

A novel constraint propagation approach for airline schedule design problem

In this paper, we propose a novel finite domain constraint programming formulation using constraint propagation (CP) approach with variable and value ordering for solving flight scheduling problem. We subsequently model the same problem using time-space network representation and integer linear programming (ILP) technique. The performance of the CP and ILP models are evaluated on simulated and a real life case. The ILP and CP models are solved using CPLEX 12.2 solver and CP optimiser 12.2, respectively. We observe that the ILP model generates better first feasible solutions, while the CP model produces faster feasible solutions. In few cases, the CP model takes longer time compared to the ILP model. For the case data with 600 aircrafts and 1,000 markets, the CP model generates feasible solution rather faster, while the ILP model could not find feasible solution in a day for defined time-space network flow problem with some additional constraints.

Solving a large-scale integrated fleet assignment and crew pairing problem

Airline schedule planning problems are typically decomposed into smaller problems, which are solved in a sequential manner, due to the complexity of the overall problems. This results in suboptimal solutions as well as feasibility issues in the consecutive phases. In this study, we address the integrated fleet assignment and crew pairing problem (IFACPP) of a European Airline. The specific network and cost structures allow us to develop novel approaches to this integrated problem. We propose an optimization-driven algorithm that can efficiently handle large scale instances of the IFACPP. We perform a computational study on real-world monthly flight schedules to test the performance of our solution method. Based on the results on instances with up to 27,500 flight legs, we show that our algorithm provides solutions with significant cost savings over the sequential approach.

AEIC 프로그램을 사용한 국내 공항 항공 온실가스 배출량 산정

항공기 배기가스가 지구의 현재와 미래의 기후에 미치는 잠재적 영향력은 항공산업계가 당면하고 있는 매우 중요한 환경 이슈 중 하나이다. 온실가스 배출의 잠재적 악영향에 대한 우려가 증가함에 따라 기후에 관한 항공기의 영향을 완화하는 수단 중 하나로 항공기 배기가스를 산정하고 예측할 필요성이 제기되고 있다. 따라서 본 연구에서는 항공기 배기가스의 산출과 저감효과 분석을 통한 항공기 온실가스 배출량을 최소화하여 미래 기후변화와 에너지 고갈에 대비하는 것을 연구목표로 한다. 우선 민간 항공 교통 스케줄을 보유한 OAG 데이터를 사용하여 우리나라 공항에 대한 항공 비행 일정을 확보하였다. 이후 미국 MIT 대학 항공 환경 실험실에서 개발한 AEIC 소프트웨어를 사용하여 2005년 한해 동안 우리나라 모든 공항에서의 3,000 ft 이하의 고도에서 이륙, 상승, 접근, 착륙단계를 포함하는 LTO 운항 시와 3,000 ft 이상의 고도에서 상승, 순항, 하강단계를 포함하는 non-LTO 운항 시의 연료소모량과 온실가스 배출량을 계산하여 결과 데이터를 각 조건별로 비교분석하였다. The potential impact of aircraft emissions on the current and projected climate of our planet is one of the more important environmental issues facing the aviation industry. Increasing concern over the potential negative effects of greenhouse gas emissions has motivated aircraft emission estimation and prediction as one of the ways to reduce aircraft emissions and mitigate the impact of aviation on climate. We obtained airline flight schedules for all the airports in Korea that are included in OAG data. Fuel burn and emission index of LTO flight which contains take off, climb and approach under 3000ft and Non LTO flight which contains climb, cruise and descent over 3000ft for all the airports in Korea in 2005 were estimated and analysed for each condition using AEIC software which has been developed by MIT Lab for Aviation and Environment.

An integrated approach for airline scheduling, aircraft fleeting and routing with cruise speed control

To place an emphasis on profound relations among airline schedule planning problems and to mitigate the effect of unexpected delays, we integrate schedule design, fleet assignment and aircraft routing problems within a daily planning horizon while passengers’ connection service levels are ensured via chance constraints. We propose a nonlinear mixed integer programming model due to the nonlinear fuel consumption and CO2 emission cost terms in the objective function, which is handled by second order conic reformulation. The key contribution of this study is to take into account the cruise time control for the first time in an integrated model of these three stages of airline operations. Changing cruise times of flights in an integrated model enables to construct a schedule to increase utilization of fuel efficient aircraft and even to decrease total number of aircraft needed while satisfying the same service level and maintenance requirements for aircraft fleeting and routing. There is a critical tradeoff between the number of aircraft needed to fulfill the required flights and overall operational expenses. We also propose two heuristic methods to solve larger size problems. Finally, computational results using real data obtained from a major U.S. carrier are presented to demonstrate potential profitability in applying the proposed solution methods.

A novel column generation strategy for large scale airline crew pairing problems

A crew pairing is a sequence of flight legs beginning and ending at the same crew domicile. Crew pairing planning is the primary cost-determining phase in airline crew scheduling. Optimizing crew pairings of an airline timetable is an extremely important process which helps to minimize operational crew costs and to maximize crew utilization.There are various restrictions imposed by regulations or company policies that must be considered and satisfied in crew pairing generation process. Keeping these restrictions and regulations in mind, the main goal of the optimization is the generation of low cost sets of valid crew pairings which cover all flights in airline's timetable.For this research study, already existing works related to crew pairing optimization are examined and a new column generation strategy, a pricing network design and a pairing elimination heuristic are developed as a contribution to the previous studies. In the proposed strategy, the main problem is modeled and solved as a set-covering problem and the pricing sub problem is modeled as a shortest-path problem which is efficiently solved over a duty-flight overnight connection graph by the combined usage of heuristic and exact algorithms. The proposed strategy has been tested with real world data obtained from Turkish Airlines and it is seen that it is capable of generating very competitive solutions compared to current practices in Turkish Airlines. It is also observed that there are various advantages of proposed solution approach such as sensitivity to penalty coefficients, generating less deadheads, very close solution times with a single threaded software and light weight hardware.

Accounting for Price Endogeneity in Airline Itinerary Choice Models: An Application to Continental U.S. Markets

Network planning models, which forecast the profitability of airline schedules, support many critical decisions, including equipment purchase decisions. Network planning models include an itinerary choice model which is used to allocate air total demand in a city pair to different itineraries. Multinomial logit (MNL) models are commonly used in practice and capture how individuals make trade-offs among different itinerary attributes; however, none that we are aware of account for price endogeneity. This study formulates an itinerary choice model that is consistent with those used by industry and corrects for price endogeneity using a control function that uses several types of instrumental variables. We estimate our models using database of more than 3 million tickets provided by the Airlines Reporting Corporation. Results based on Continental U.S. markets for May 2013 departures show that models that fail to account for price endogeneity overestimate customers’ value of time and result in biased price estimates and incorrect pricing recommendations. The size and comprehensiveness of our database allows us to estimate highly refined departure time of day preference curves that account for distance, direction of travel, the number of time zones traversed, departure day of week and itinerary type (outbound, inbound or one-way). These time of day preference curves can be used by airlines, researchers, and government organizations in the evaluation of different policies such as congestion pricing.

Investigation of hidden markov model for the tuning of metaheuristics in airline scheduling problems

Abstract: The tuning approach consists in finding the most suitable configuration of an algorithm for solving a given problem. Machine learning methods are usually used to automate this process. They may enable to construct robust autonomous artifacts whose behavior becomes increasingly expert. This paper focuses on the restriction of this general problem to the field of air planning and more specifically the crew scheduling problem. Metaheuristics are widely used to solve this problem. Our approach consists of using hidden markov model to find the best configuration of the algorithm based on the estimation of the most likely state. The experiment consists of finding the best parameter values of the particle swarm optimization algorithm for the crew scheduling problem. Our approach has shown that it can be a promising solution for automatic optimization of airline scheduling problems.

Accounting for Price Endogeneity in Airline Itinerary Choice Models: An Application to Continental U.S. Markets

Network planning models, which forecast the profitability of airline schedules, support many critical decisions, including equipment purchase decisions. Network planning models include an itinerary choice model that is used to allocate air total demand in a city pair to different itineraries. Multinomial logit (MNL) models are commonly used in practice and capture how individuals make trade-offs among different itinerary attributes; however, none that we are aware of account for price endogeneity. This study formulates an itinerary choice model that is consistent with those used by industry and corrects for price endogeneity using a control function that uses several types of instrumental variables. We estimate our model using a database of more than 3 million tickets provided by the Airlines Reporting Corporation. Results based on Continental U.S. markets for May 2013 departures show that models that fail to account for price endogeneity overestimate customers’ value of time and result in biased price estimates and incorrect pricing recommendations. The size and comprehensiveness of our database allows us to estimate highly refined departure time of day preference curves that account for distance, direction of travel, number of time zones traversed, departure day of week and itinerary type (outbound, inbound or one-way). These time of day preference curves can be used by airlines, researchers, and government organizations in the evaluation of different policies such as congestion pricing.

An Integrated Scheduling and Operations Approach to Airport Congestion Mitigation

Most flight delays are created by imbalances between demand and capacity at the busiest airports. Absent large increases in capacity, airport congestion can only be mitigated through scheduling interventions or improved capacity utilization. This paper presents an integrated approach that jointly optimizes the airport’s flight schedule at the strategic level and the utilization of airport capacity at the tactical level, subject to scheduling, capacity, and delay-reduction constraints. The capacity-utilization part involves controlling the runway configuration and the balance of arrival and departure service rates to minimize congestion costs. The schedule optimization reschedules a selected set of flights to reduce the demand-capacity mismatches while minimizing interference with airline competitive scheduling. We develop an original iterative solution algorithm that integrates a stochastic queuing model of airport congestion, a dynamic programming model of capacity utilization, and an integer programming model of scheduling interventions. The algorithm is shown to converge in reasonable computational times. Extensive computational results for JFK Airport suggest that substantial delay reductions can be achieved through limited changes in airline schedules. It is also shown that the proposed integrated approach to airport congestion mitigation performs significantly better than the typical sequential approach, where scheduling and operational decisions are made separately.

Patient Safety Reporting: Lessons From the Blind Men and the Elephant

Keywords: editorial / personal viewpoint; organ procurement and allocation; organ transplantation in general; business / management; United Network for Organ Sharing (UNOS); quality of care / care delivery; patient safety

Hubs at risk: Exposure of Europe's largest hubs to competition on transfer city Pairs

Hubs are airports used by airlines as transfer points to get passengers to their destinations. Each of the five largest European hubs – Amsterdam, Charles de Gaulle, Frankfurt, Heathrow, and Madrid – is closely associated with one former national flag carrier. Some concerns exist in Europe that the expansion of the Gulf carriers with their hubs in Dubai, Abu Dhabi and Doha threatens the existence of European hubs regarding transfer city pairs that include at least one long-haul leg. Our paper examines the actual exposure to competition by combining airline schedules data with methodology to measure competitive transfer connections. We provide the percentage of the transfer city pairs of the five largest European hubs that is exposed to competition. Further, we identify the main competitors to each of these hubs. One important result of our paper is that despite the increasing market share of Gulf carriers, the main competition for transfer traffic is still among the five largest European hubs, with Munich and Istanbul being another two strong contenders. Hence, our paper puts into perspective the competitive risk posed by Gulf carriers and their hubs.

Reprint of “Modelling the resilience, friability and costs of an air transport network affected by a large-scale disruptive event”

This paper deals with developing a methodology for estimating the resilience, friability, and costs of an air transport network affected by a large-scale disruptive event. The network consists of airports and airspace/air routes between them where airlines operate their flights. Resilience is considered as the ability of the network to neutralize the impacts of disruptive event(s). Friability implies reducing the network’s existing resilience due to removing particular nodes/airports and/or links/air routes, and consequently cancelling the affected airline flights. The costs imply additional expenses imposed on airports, airlines, and air passengers as the potentially most affected actors/stakeholders due to mitigating actions such as delaying, cancelling and rerouting particular affected flights. These actions aim at maintaining both the network’s resilience and safety at the acceptable level under given conditions.Large scale disruptive events, which can compromise the resilience and friability of a given air transport network, include bad weather, failures of particular (crucial) network components, the industrial actions of the air transport staff, natural disasters, terrorist threats/attacks and traffic incidents/accidents.The methodology is applied to the selected real-life case under given conditions. In addition, this methodology could be used for pre-selecting the location of airline hub airport(s), assessing the resilience of planned airline schedules and the prospective consequences, and designing mitigating measures before, during, and in the aftermath of a disruptive event. As such, it could, with slight modifications, be applied to transport networks operated by other transport modes.

Modelling the resilience, friability and costs of an air transport network affected by a large-scale disruptive event

This paper deals with developing a methodology for estimating the resilience, friability, and costs of an air transport network affected by a large-scale disruptive event. The network consists of airports and airspace/air routes between them where airlines operate their flights. Resilience is considered as the ability of the network to neutralize the impacts of disruptive event(s). Friability implies reducing the network’s existing resilience due to removing particular nodes/airports and/or links/air routes, and consequently cancelling the affected airline flights. The costs imply additional expenses imposed on airports, airlines, and air passengers as the potentially most affected actors/stakeholders due to mitigating actions such as delaying, cancelling and rerouting particular affected flights. These actions aim at maintaining both the network’s resilience and safety at the acceptable level under given conditions.Large scale disruptive events, which can compromise the resilience and friability of a given air transport network, include bad weather, failures of particular (crucial) network components, the industrial actions of the air transport staff, natural disasters, terrorist threats/attacks and traffic incidents/accidents.The methodology is applied to the selected real-life case under given conditions. In addition, this methodology could be used for pre-selecting the location of airline hub airport(s), assessing the resilience of planned airline schedules and the prospective consequences, and designing mitigating measures before, during, and in the aftermath of a disruptive event. As such, it could, with slight modifications, be applied to transport networks operated by other transport modes.

Robust Airline Scheduling with Controllable Cruise Times and Chance Constraints

Robust airline schedules can be considered as flight schedules that are likely to minimize passenger delay. Airlines usually add an additional time—e.g., schedule padding—to scheduled gate-to-gate flight times to make their schedules less susceptible to variability and disruptions. There is a critical trade-off between any kind of buffer time and daily aircraft productivity. Aircraft speed control is a practical alternative to inserting idle times into schedules. In this study, block times are considered in two parts: Cruise times that are controllable and non-cruise times that are subject to uncertainty. Cruise time controllability is used together with idle time insertion to satisfy passenger connection service levels while ensuring minimum costs. To handle the nonlinearity of the cost functions, they are represented via second-order conic inequalities. The uncertainty in non-cruise times is modeled through chance constraints on passenger connection service levels, which are then expressed using second-order conic inequalities. Overall, it is shown, that a 2% increase in fuel costs cuts down 60% of idle time costs. A computational study shows that exact solutions can be obtained by commercial solvers in seconds for a single-hub schedule and in minutes for a four-hub daily schedule of a major U.S. carrier.

Clustered airline flight scheduling: Evidence from airline deregulation in Korea

This paper explores the impacts of competition level on airline scheduling in the Korean domestic short-haul routes where a hub-and-spoke system is not the optimal air transport network strategy. The empirical findings using the Korean airline panel data for the period 2006–2010 suggest that competition leads to less differentiated departure flight times as expected from spatial competition theory. Unlike the previous study on the U.S airline industry, the degree of this tendency for less differentiation differs across the type of routes: the Jeju island routes (leisure type) and the inland routes (business type), in the deregulated period. Following the May 2008 Deregulation Act we find an increasingly clustered pattern of airline scheduling in the Jeju island routes where there have been competitive pressures associated with new low cost entrants. This recent evidence would imply that airline carriers strategically schedule departure flight times and allocate flights between routes as competition increases in the deregulated period.

A 2-level Metaheuristic for the Set Covering Problem

Metaheuristics are solution methods which combine local improvement procedures and higher strategies for solving combinatorial and nonlinear optimization problems. In general, metaheuristics require an important amount of effort focused on parameter setting to improve its performance. In this work a 2-level metaheuristic approach is proposed so that Scatter Search and Ant Colony Optimization act as “low level metaheuristics, whose parameters are set by a “higher level Genetic Algorithm during execution, seeking to improve the performance and to reduce the maintenance. The Set Covering Problem is taken as reference since is one of the most important optimization problems, serving as basis for facility location problems, airline crew scheduling, nurse scheduling, and resource allocation.

Airline Fleet Assignment and Schedule Planning

Airline fleet assignment plays important role for efficient operation of airlines. In order to save costs from the procurement and maintenance of extra aircrafts, the airline operators would always seek to minimise the fleet size, while at the same time being able to operate all the flights without shortage of aircraft. Therefore, the daily flight schedule of the airline must be prepared in such a way that the destinations can be served with minimum number of aircrafts. Considering this, there is huge prospect of using optimisation techniques to select the optimum mix of various types of aircrafts in the airline fleet and thereby to produce an optimum flight schedule.

Runway Assignments That Minimize Terminal Airspace and Airport Surface Emissions

Air traffic operations at and around major airports in the United States are in need of improvement. From the perspective of arrivals, when one or more runways are in great demand, unnecessary delay and emissions occur during peak periods at major airports while other runways at the same airport are operating under capacity. The primary cause of this imbalance in runway utilization is that traffic flow into and out of terminal areas is asymmetric (as a result of airline scheduling practices) and arrivals are typically assigned to the runway nearest the fix through which they enter the terminal area. From the perspective of departures, delays and emissions are incurred because arrivals take precedence over departures with regard to the utilization of runways (despite the absence of binding safety constraints) and because arrival trajectories often include level segments that ensure “procedural separation” from arriving traffic while planes are not allowed to climb unrestricted along the most direct path to their destination. This paper presents an optimization model for simultaneously assigning aircraft to runways and scheduling the arrival and departure operations on these runways such that the total emissions produced in the terminal area and on the airport surface are minimized. Model constraints include such real-world considerations as the minimum separation required between successive aircraft and the precedence relationships that aircraft flying a common route take. An analysis of simulation results indicates that implementation of this model could result in as much as a 30% reduction in arrival emissions with a corresponding 3% reduction in departure emissions.

Congestion Mitigation at John F. Kennedy International Airport in New York City

With the significant growth in air traffic experienced during the past few decades, airport capacity has become an increasingly costly constraint. Flight delays reached record-high levels in 2007, with a nationwide impact estimated at more than $30 billion for that calendar year. At airports where capacity expansion and improvements in operational efficiency are not feasible, congestion could be mitigated in the short and medium terms through implementation of schedule coordination mechanisms. Such measures essentially reduce peak hour scheduling levels; however, these measures have been criticized for the constraints that they might create on airline scheduling. This paper presents a schedule coordination model that reduces flight delays while minimizing interference with airline scheduling and then applies the model to one of the most congested U.S. airports, John F. Kennedy International Airport in New York City. The analysis suggests that it may be possible to reduce peak arrival and departure delays by more than 30% and 50%, respectively, without eliminating flights, aircraft connections, or passenger connections and without modifying the scheduled departure and arrival times of any flight by more than 30 min. These results underscore the potential of schedule coordination as a means of achieving substantial congestion cost savings at the busiest U.S. airports. Opportunities and challenges associated with the implementation of such a mechanism are discussed.

A Rolling Horizon Based Algorithm for Solving Integrated Airline Schedule Recovery Problem

A Rolling Horizon Based Algorithm for Solving Integrated Airline Schedule Recovery Problem