Solving a robust airline crew pairing problem with column generation

Muter, İbrahim; Birbil, Ş. İlker; Bülbül, Kerem; Şahin, Güvenç; Yenigün, Hüsnü; Taş, Duygu; Tüzün, Dilek

doi:10.1016/j.cor.2010.11.005

Cited by 50 publications

(29 citation statements)

References 23 publications

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“…Schedule stability concerns the ability of a schedule to remain valid under a certain degree of disruption . Schedule flexibility concerns the ability to modify a schedule when disruption occurs . To capture uncertainties and make the flight schedule more robust, buffer time is a very useful tool for absorbing flight delays.…”

Section: Introductionmentioning

confidence: 99%

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Cascading Delay Risk of Airline Workforce Deployments with Crew Pairing and Schedule Optimization

Chung

Chan

2016

Risk Analysis

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This article concerns the assignment of buffer time between two connected flights and the number of reserve crews in crew pairing to mitigate flight disruption due to flight arrival delay. Insufficient crew members for a flight will lead to flight disruptions such as delays or cancellations. In reality, most of these disruption cases are due to arrival delays of the previous flights. To tackle this problem, many research studies have examined the assignment method based on the historical flight arrival delay data of the concerned flights. However, flight arrival delays can be triggered by numerous factors. Accordingly, this article proposes a new forecasting approach using a cascade neural network, which considers a massive amount of historical flight arrival and departure data. The approach also incorporates learning ability so that unknown relationships behind the data can be revealed. Based on the expected flight arrival delay, the buffer time can be determined and a new dynamic reserve crew strategy can then be used to determine the required number of reserve crews. Numerical experiments are carried out based on one year of flight data obtained from 112 airports around the world. The results demonstrate that by predicting the flight departure delay as the input for the prediction of the flight arrival delay, the prediction accuracy can be increased. Moreover, by using the new dynamic reserve crew strategy, the total crew cost can be reduced. This significantly benefits airlines in flight schedule stability and cost saving in the current big data era.

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Section: Introductionmentioning

confidence: 99%

“…Lastly, some studies proposed crew swapping, also called duty swapping. The idea is to swap the duties between two crews to cover one with another . In our article, buffer time is determined by a cascade neural network (CNN), while the reserve crew is determined based on a dynamic reserve crew strategy.…”

Section: Introductionmentioning

confidence: 99%

Cascading Delay Risk of Airline Workforce Deployments with Crew Pairing and Schedule Optimization

Chung

Chan

2016

Risk Analysis

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“…If long enough, a delay in the planned arrival time of a vehicle may, in turn, affect also the departure time of the successive trips assigned to the same vehicle. The second type of disruption is caused by the excess demand for a particular origin-destination pair that mandates the planners to insert an extra trip into the schedule at the operation phase (Tekiner et al 2009, Muter et al 2013b. Both types of disruptions are quite common in countries with a large bus transportation network like Turkey.…”

Section: Introductionmentioning

confidence: 99%

Managing disruptions in the multi-depot vehicle scheduling problem

Ucar

Birbil

Muter

2017

Transportation Research Part B: Methodological

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We consider two types of disruptions arising in the multi-depot vehicle scheduling; the delays and the extra trips. These disruptions may or may not occur during operations, and hence they need to be indirectly incorporated into the planned schedule by anticipating their likely occurence times. We present a unique recovery method to handle these potential disruptions. Our method is based on partially swapping two planned routes in such a way that the effect on the planned schedule is minimal, if these disruptions are actually realized. The mathematical programming model for the multi-depot vehicle scheduling problem, which incorporates these robustness considerations, possesses a special structure. This special structure causes the conventional column generation method fall short as the resulting problem grows also row-wise when columns are generated. We design an exact simultaneous column-and-row generation algorithm to find a valid lower-bound. The novel aspect of this algorithm is the pricing subproblem, which generates pairs of routes that form recovery solutions. Compromising on exactness, we modify this algorithm in order to enable it to solve practical-sized instances efficiently. This heuristic algorithm is shown to provide very tight bounds on the randomly generated instances in a short computation time.

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“…Column generation has been successfully used in many studies to solve complicated integer programming problems [6,7]. Therefore, it is very important to develop an efficient algorithm for the shortest path problem with battery renewal.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

The Shortest Path Problems in Battery-Electric Vehicle Dispatching with Battery Renewal

Huang

2016

Sustainability

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Electric vehicles play a key role for developing an eco-sustainable transport system. One critical component of an electric vehicle is its battery, which can be quickly charged or exchanged before it runs out. The problem of electric vehicle dispatching falls into the category of the shortest path problem with resource renewal. In this paper, we study the shortest path problems in (1) electric transit bus scheduling and (2) electric truck routing with time windows. In these applications, a fully-charged battery allows running a limited operational distance, and the battery before depletion needs to be quickly charged or exchanged with a fully-charged one at a battery management facility. The limited distance and battery renewal result in a shortest path problem with resource renewal. We develop a label-correcting algorithm with state space relaxation to find optimal solutions. In the computational experiments, real-world road geometry data are used to generate realistic travel distances, and other types of data are obtained from the real world or randomly generated. The computational results show that the label-correcting algorithm performs very well.

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Solving a robust airline crew pairing problem with column generation

Cited by 50 publications

References 23 publications

Cascading Delay Risk of Airline Workforce Deployments with Crew Pairing and Schedule Optimization

Cascading Delay Risk of Airline Workforce Deployments with Crew Pairing and Schedule Optimization

Managing disruptions in the multi-depot vehicle scheduling problem

The Shortest Path Problems in Battery-Electric Vehicle Dispatching with Battery Renewal

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