Airline fleet assignment and schedule design : integrated models and algorithms

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.Includes bibliographical references (p. 187-192).In scheduled passenger air transportation, airline profitability is critically influenced by the airline's ability to construct flight schedules containing flights at desirable times in profitable markets. In this dissertation, we study two elements of the schedule generation process, namely, schedule design and fleet assignment. The schedule design problem involves selecting an optimal set of flight legs to be included in the schedule, while the fleet assignment problem involves assigning aircraft types (or fleets) to flight legs to maximize revenues and minimize operating costs simultaneously. With the fleet assignment problem, we investigate the issues of network effects, spill, and recapture. On a constrained flight leg in which demand exceeds capacity, some passengers are not accommodated, or spilled. When passengers travel on two or more constrained legs, flight leg interdependencies or network effects arise because spill can occur on any of these legs. In most basic fleet assignment models, simplistic modeling of network effects and recapture leads to sometimes severe, miscalculations of revenues. Recapture occurs when some of the spilled passengers are re-accommodated on alternate itineraries in the system. In this dissertation, we develop new fleet assignment models that capture network effects, spill, and recapture. Another benefit of one of our models is its tractability and potential for further integration with other schedule planning steps.(cont.) Our study shows that the benefits of modeling these elements can be as large as $100 million annually for a major U.S. airline. In addition, we show that modeling flight leg interdependence is more important than demand stochasticity for hub-and-spoke fleet assignment problems. We develop two models for schedule design, one assuming that the market share of an airline remains constant with schedule changes; and the other assuming that market share varies with schedule changes. The constant market share model, while less precise in its modeling, is much easier to solve than the variable market share model. We estimate that the potential benefits of these models range from$100 to $350 million annually.Manoj Lohatepanont.Sc.D

Increasing Collegiate Flight Training Fleet Utilization Through the Use of an Aircraft Assignment Algorithm

The operational efficiency of fleet aircraft employed for student flight training in collegiate aviation programs is strongly influenced by scheduling, among other factors. The average utilization rate for the fleet operated by the Purdue University School of Aviation and Transportation Systems was found to be 24% (Avery, 2014), and there is no data to suggest that that this rate is atypical in similar institutional programs. Mott and Bullock (2015) identified several means by which the utilization rate could be increased, and improvements in the dispatch and scheduling process were a key component of those recommendations. This article describes a scheduling algorithm that was implemented at Purdue University in the fall semester of 2015. The algorithm is a linear programming technique that incorporates optimization constraints unique to collegiate flight training operations. The resulting improvements in aircraft utilization will facilitate an increased matriculation rate of students into the flight program, thereby allowing the allocation of fixed costs over a wider user base and the reduction of overall program fees for all students. Those improvements are validated through measurement of the reduction of the cumulative turn times between aircraft operations

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

Integrating the fleet assignment model with uncertain demand

This thesis was submitted for the degree of Master of Philosophy and awarded by Brunel University.One of the main challenges facing the airline industry is planning under uncertainty, especially in the context of schedule disruptions. The robust models and solution algorithms that have been proposed and developed to handle the uncertain parameters will be discussed. Fleet assignment models (FAM) are used by many airlines to assign aircraft to fights in a schedule to maximize profit. In the context of FAM, the goal of robustness is to produce solutions that perform well relative to uncertainties in demand and operation. In this thesis, we introduce new FAMs (i.e. DFAM1 and DFAM2) that tackles the common problem associated with aircraft utilization. Subsequently, stochastic programming (SP) is presented as a method of choice for the research. Through the use of a two-stage SP with recourse technique, the DFAMs are extended to SP-FAMs (SP-FAM1 and SP-FAM2). The main distinction of the SP-FAM compared with other FAMs is that, given a stochastic passenger demand, it gives a strategic fleet assignment solution that hedges against all possible tactical solutions. In addition, we have a tactical solution for every scenario. In generating the demand scenarios, we use a network-simulation model embedded with a time-series engine that gives a snapshot of one week that is representative of any other week of the scheduling season. We later outline the approach of solving the SP-FAMs where the schedule is compacted through several preprocessing steps before inputting it into SAS-AMPL converter. The SAS-AMPL converter prepares all the data into readable AMPL format. Finally, we execute the optimizer using a FortMP solver (integrated in AMPL) that invokes branch-and-bound algorithm. We give a proof of concept using real data from a Middle East airline. Our investigations establish clear benefits of the recourse FAM compared to alternative models. Finally, we propose areas of future research to improve SP-FAM robustness through solution algorithms, revenue management (RM) effects, calibration of network-simulation models and system integration

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

Integrated fleet assignment with cargo routing

Master'sMASTER OF ENGINEERIN

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

Mitigating airport congestion : market mechanisms and airline response models

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaves 157-165).Efficient allocation of scarce resources in networks is an important problem worldwide. In this thesis, we focus on resource allocation problems in a network of congested airports. The increasing demand for access to the world's major commercial airports combined with the limited operational capacity at many of these airports have led to growing air traffic congestion resulting in several billion dollars of delay cost every year. In this thesis, we study two demand-management techniques -- strategic and operational approaches -- to mitigate airport congestion. As a strategic initiative, auctions have been proposed to allocate runway slot capacity. We focus on two elements in the design of such slot auctions -- airline valuations and activity rules. An aspect of airport slot market environments, which we argue must be considered in auction design, is the fact that the participating airlines are budget-constrained. -- The problem of finding the best bundle of slots on which to bid in an iterative combinatorial auction, also called the preference elicitation problem, is a particularly hard problem, even more in the case of airlines in a slot auction. We propose a valuation model, called the Aggregated Integrated Airline Scheduling and Fleet Assignment Model, to help airlines understand the true value of the different bundles of slots in the auction. This model is efficient and was found to be robust to data uncertainty in our experimental simulations.(cont.) -- Activity rules are checks made by the auctioneer at the end of every round to suppress strategic behavior by bidders and to promote consistent, continual preference elicitation. These rules find applications in several real world scenarios including slot auctions. We show that the commonly used activity rules are not applicable for slot auctions as they prevent straightforward behavior by budget-constrained bidders. We propose the notion of a strong activity rule which characterizes straightforward bidding strategies. We then show how a strong activity rule in the context of budget-constrained bidders (and quasilinear bidders) can be expressed as a linear feasibility problem. This work on activity rules also applies to more general iterative combinatorial auctions.We also study operational (real-time) demand-management initiatives that are used when there are sudden drops in capacity at airports due to various uncertainties, such as bad-weather. We propose a system design that integrates the capacity allocation, airline recovery and inter-airline slot exchange procedures, and suggest metrics to evaluate the different approaches to fair allocations.by Pavithra Harsha.Ph.D

Non-linear integer programming fleet assignment model

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering. University of the Witwatersrand, Johannesburg, 2016Given a flight schedule with fixed departure times and cost, solving the fleet assignment problem assists airlines to find the minimum cost or maximum revenue assignment of aircraft types to flights. The result is that each flight is covered exactly once by an aircraft and the assignment can be flown using the available number of aircraft of each fleet type. This research proposes a novel, non-linear integer programming fleet assignment model which differs from the linear time-space multi-commodity network fleet assignment model which is commonly used in industry. The performance of the proposed model with respect to the amount of time it takes to create a flight schedule is measured. Similarly, the performance of the time-space multicommodity fleet assignment model is also measured. The objective function from both mathematical models is then compared and results reported. Due to the non-linearity of the proposed model, a genetic algorithm (GA) is used to find a solution. The time taken by the GA is slow. The objective function value, however, is the same as that obtained using the time-space multi-commodity network flow model. The proposed mathematical model has advantages in that the solution is easier to interpret. It also simultaneously solves fleet assignment as well as individual aircraft routing. The result may therefore aid in integrating more airline planning decisions such as maintenance routing.MT201