OPTIMIZATION APPROACHES TO AIRLINE INDUSTRY CHALLENGES: Airline Schedule Planning and Recovery

The airline industry has a long history of developing and applying optimization approaches to their myriad of scheduling problems, including designing flight schedules that maximize profitability while satisfying rules related to aircraft maintenance; generating cost-minimizing, feasible work schedules for pilots and flight attendants; and identifying implementable, low-cost changes to aircraft and crew schedules as disruptions render the planned schedule inoperable. The complexities associated with these problems are immense, including long-and short-term planning horizons; and multiple resources including aircraft, crews, and passengers, all operating over shared airspace and airport capacity. Optimization approaches have played an important role in overcoming this complexity and providing effective aircraft and crew schedules. Historical optimization-based approaches, however, often involve a sequential process, first generating aircraft schedules and then generating crew schedules. Decisions taken in the first steps of the process limit those that are possible in subsequent steps, resulting in overall plans that, while feasible, are typically sub-optimal. To mitigate the myopic effects of sequential solutions, researchers have developed extended models that begin to integrate som

Robust airline schedule design in a dynamic scheduling environment

In the past decade, major airlines in the US have moved from banked hub-and-spoke operations to de-banked hub-and-spoke operations in order to lower operating costs. In Jiang and Barnhart (2009), it is shown that dynamic airline scheduling, an approach that makes minor adjustments to flight schedules in the booking period by re-fleeting and re-timing flight legs, can significantly improve utilization of capacity and hence increase profit. In this paper, we develop robust schedule design models and algorithms to generate schedules that facilitate the application of dynamic scheduling in de-banked hub-and-spoke operations. Such schedule design approaches are robust in the sense that the schedules produced can more easily be manipulated in response to demand variability when embedded in a dynamic scheduling environment. In our robust schedule design model, we maximize the number of potentially connecting itineraries weighted by their respective revenues. We provide two equivalent formulations of the robust schedule design model and develop a decomposition-based solution approach involving a variable reduction technique and a variant of column generation. We demonstrate, through experiments using data from a major U.S. airline that the schedule generated can improve profitability when dynamic scheduling is applied. It is also observed that the greater the demand variability, the more profit our robust schedules achieve when compared to existing ones

Attractiveness-based airline network models with embedded spill and recapture

Purpose: In airline revenue management, the modeling of the spill and recapture effects is essential for an accurate estimation of the passenger flow and the revenue in a flight network. However, as most current approaches toward spill and recapture involve either non-linearity or a tremendous amount of additional variables, it is computationally intractable to apply those techniques to the classical network design and capacity planning models. Design/methodology: We present a new framework that incorporates the spill and recapture effects, where the spill from an itinerary is recaptured by other itineraries based on their attractiveness. The presented framework distributes the accepted demand of an itinerary according to the currently available itineraries, without adding extra variables for the recaptured spill. Due to its compactness, we integrate the framework with the classical capacity planning and network design models. Findings: Our preliminary computational study shows an increase of 1.07% in profitability anda better utilization of the network capacity, on a medium-size North American airline provided by Sabre Airline Solutions. Originality/value: Our investigation leads to a holistic model that tackles the network design and capacity planning simultaneously with an accurate modeling of the spill and re- capture effects.Furthermore, the presented framework for spill and recapture is versatile and can be easily applied to other disciplines such as the hospitality industry and product line design (PLD) problems.Peer Reviewe

Integrating Strategic and Tactical Rolling Stock Models with Cyclical Demand

In the transportation industry, companies position rolling stock where it is likely to be needed in the face of a pronounced weekly cyclical demand pattern in orders. Strategic policies based on assumptions of repetition of cyclical weekly patterns set rolling stock targets; during tactical execution, a myriad dynamic influences cause deviations from strategically set targets. We find that optimal strategic plans do not agree with results of tactical modeling; strategic results are in fact suboptimal in many tactical situations. We discuss managerial implications of this finding and how the two modeling paradigms can be reconciled

Programació dinàmica de vols atenent a demandes estocàstiques

La programació de vols és una de les principals activitats de planificació que es duen a terme en una companyia aèria; el resultat d'aquesta tasca té implicacions que transgredeixen l'àmbit operacional i esdevé un factor determinant de cara a millorar la competitivitat en el sector del transport aeri. Sovint, la naturalesa estratègica d'aquesta activitat implica que es realitzi a una antelació en què la previsió respecte la demanda de passatgers és força difusa i pot resultar, el dia d'operació dels vols, en manques d'eficiència al deixar vols amb seients buits i a l'haver-ne assignat d'altres amb avions de massa poca capacitat. Així doncs, en aquest projecte es presenta una metodologia (algoritme DACRA) que executant petites alteracions sobre la planificació de vols inicial, realitza ajustaments de capacitat, possibilitant l'adaptació contínua del programa de vols a les actualitzacions disponibles respecte la previsió de demanda de passatgers per a una data concreta, tot millorant-ne els beneficis operatius i preservant que les noves solucions generades siguin operativament factibles

Methods for Improving Robustness and Recovery in Aviation Planning.

In this dissertation, we develop new methods for improving robustness and recovery in aviation planning. In addition to these methods, the contributions of this dissertation include an in-depth analysis of several mathematical modeling approaches and proof of their structural equivalence. Furthermore, we analyze several decomposition approaches, the difference in their complexity and the required computation time to provide insight into selecting the most appropriate formulation for a particular problem structure. To begin, we provide an overview of the airline planning process, including the major components such as schedule planning, fleet assignment and crew planning approaches. Then, in the first part of our research, we use a recursive simulation-based approach to evaluate a flight schedule's overall robustness, i.e. its ability to withstand propagation delays. We then use this analysis as the groundwork for a new approach to improve the robustness of an airline's maintenance plan. Specifically, we improve robustness by allocating maintenance rotations to those aircraft that will most likely benefit from the assignment. To assess the effectiveness of our approach, we introduce a new metric, maintenance reachability, which measures the robustness of the rotations assigned to aircraft. Subsequently, we develop a mathematical programming approach to improve the maintenance reachability of this assignment. In the latter part of this dissertation, we transition from the planning to the recovery phase. On the day-of-operations, disruptions often take place and change aircraft rotations and their respective maintenance assignments. In recovery, we focus on creating feasible plans after such disruptions have occurred. We divide our recovery approach into two phases. In the first phase, we solve the Maintenance Recovery Problem (MRP), a computationally complex, short-term, non-recurrent recovery problem. This research lays the foundation for the second phase, in which we incorporate recurrence, i.e. the property that scheduling one maintenance event has a direct implication on the deadlines for subsequent maintenance events, into the recovery process. We recognize that scheduling the next maintenance event provides implications for all subsequent events, which further increases the problem complexity. We illustrate the effectiveness of our methods under various objective functions and mathematical programming approaches.Ph.D.Industrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91539/1/mlapp_1.pd

Quantum Computing for Airline Planning and Operations

Classical algorithms and mathematical optimization techniques have beenused extensively by airlines to optimize their profit and ensure that regulationsare followed. In this thesis, we explore which role quantum algorithmscan have for airlines. Specifically, we have considered the two quantum optimizationalgorithms; the Quantum Approximate Optimization Algorithm(QAOA) and Quantum Annealing (QA). We present a heuristic that integratesthese quantum algorithms into the existing classical algorithm, whichis currently employed to solve airline planning problems in a state-of-the-artcommercial solver. We perform numerical simulations of QAOA circuits andfind that linear and quadratic algorithm depth in the input size can be requiredto obtain a one-shot success probability of 0.5. Unfortunately, we areunable to find performance guarantees. Finally, we perform experiments withD-wave’s newly released QA machine and find that it outperforms 2000Q formost instances

Robust planning in scheduled passenger traffic with applied stochastic programming and integrated risk management

Der Planungsprozess im fahrplanbasierten Passagierverkehr ist eine sehr komplexe Aufgabe und viele Entscheidungen im Planungsprozess müssen unter Unsicherheit getroffen werden. In der langfristigen Planung müssen Fluggesellschaften und Unternehmen des ÖPNVs beispielsweise mit einer Nachfrage-und Treibstoffpreisunsicherheit umgehen. In der kurzfristigen Planung verursachen unvorhersehbare Störungen aufgrund von Wetterbedingungen oder Verkehrsaufkommen Abweichungen vom Plan. Daher ist der Gewinn der Unternehmen in hohem Maße abhängig von der Entwicklung unsicher Parameter. Zum Begrenzen des Risikos in schlechten Szenarien müssen robustere Pläne erstellt werden. Die Robustheit der Pläne kann durch die Integration von Risikomanagement in den Planungsprozess verbessert werden. Die Risiken können mit operativen Methoden, wie z. B. dem Wechsel eines Flugzeugtyps oder der Erhöhung der Pufferzeit zwischen zwei Busfahrten sowie mit kontraktuellen Methoden, wie Hedging der Treibstoffpreise verringert werden. Das Ziel ist, robustere Lösungen für den Planungsprozess im fahrplanbasierten Passagierverkehr zu finden. Bestehende Optimierungsmodelle wurden weiterentwickelt oder neue Modelle wurden von Grund auf entwickelt. Eine integrierte Risikomanagementstrategie wurde in diese Modelle integriert und Fallstudien wurden verwendet, um die Vorteile der robusten Planung nachzuweisen.The planning process in scheduled passenger traffic is a very complex task and many decisions in the planning process have to be fixed under uncertainty. In long-term planning airlines and public transport companies have to cope with demand and fuel price uncertainty, for example. In short-term planning unforeseeable disruptions due to weather conditions or traffic density cause deviations from the plan. As a result, the profit of companies operating in scheduled passenger traffic highly depends on the development of uncertain parameters. To manage and limit the risk of bad scenarios, more robust plans have to be created. The robustness of the plans can be increased by integrating risk management into the planning process. The risks can be decreased with operational methods, such as changing the aircraft type of a flight or increasing the buffer time between two bus trips, as well as with contractual methods, such as hedging fuel prices. The objective is to find more robust solutions for the planning process in scheduled passenger traffic. Existing optimization models are re-developed or new models are developed from scratch, an integrated risk management strategy is integrated into these models, and case studies are used to show the advantages for robust planning.Tag der Verteidigung: 26.09.2012Paderborn, Univ., Diss., 201

Dynamic airline scheduling and robust airline schedule de-peaking

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2006.Includes bibliographical references (p. 151-156).Demand stochasticity is a major challenge for the airlines in their quest to produce profit maximizing schedules. Even with an optimized schedule, many flights have empty seats at departure, while others suffer a lack of seats to accommodate passengers who desire to travel. Recognizing that demand forecast quality for a particular departure date improves as the date comes close, we tackle this challenge by developing a dynamic scheduling approach that re-optimizes elements of the flight schedule during the passenger booking period. The goal is to match capacity to demand, given the many operational constraints that restrict possible assignments. We introduce flight re-timing as a dynamic scheduling mechanism and develop a re-optimization model that combines both flight re-timing and flight re-fleeting. Our re-optimization approach, re-designing the flight schedule at regular intervals, utilizes information from both revealed booking data and improved forecasts available at later re-optimizations. Experiments are conducted using data from a major U.S. airline. We demonstrate that significant potential profitability improvements are achievable using this approach.(cont.) We complement this dynamic re-optimization approach with models and algorithms to de-peak existing hub-and-spoke flight schedules so as to maximize future dynamic scheduling capabilities. In our robust de-peaking approach, we begin by solving a basic de-peaking model to provide a basis for comparison of the robust de-peaked schedule we later generate. We then present our robust de-peaking model to produce a schedule that maximizes the weighted sum of potentially connecting itineraries and attains at least the same profitability as the schedule produced by the basic de-peaking model. We provide several reformulations of the robust de-peaking model and analyze their properties. To address the tractability issue, we construct a restricted model through an approximate treatment of the profitability requirement. The restricted model is solved by a decomposition based solution approach involving a variable reduction technique and a new form of column generation. We demonstrate, through experiments using data from a major U.S. airline, that the schedule generated by our robust de-peaking approach achieves improved profitability.by Hai Jiang.Ph.D