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We present a methodology for deriving robust airline schedules that are not vulnerable to disruptions caused by bad weather. In this methodology, the existing schedule is partitioned into independent sub-schedules or layers - prioritized on the basis of revenue - that provide airlines with a clear delay/cancellation policy and may enable them to market and sell tickets for flight legs based oil passenger preference for reliability. We present three different ways to incorporate degradability into the scheduling process: (1) between flight scheduling and fleet assignment (degradable schedule partitioning model), (2) with fleet assignment (degradable fleet assignment model), and (3) with aircraft routing (degradable aircraft routing model). Each problem is modeled as an integer program. Search algorithms are applied to the degradable aircraft routing model, which has a large number of decision variables. Results indicate that we can successfully assign flight legs with high revenue itineraries in the higher priority layer without adding aircraft or changing the schedule, and differentiate the service quality for passengers in different priority layers. Passengers in the high priority layers have much less delay and fewer cancellations than passengers in low priority layers even during the bad weather. In terms of recovery cost, which includes revenue lost, operational cost saving and crew delay cost, degradable airline schedules can save up to $30,000 per day. Degradable airline schedules have cost saving effect, especially when an airport with a high capacity reduction in bad weather is affected by bad weather.
The global airline industry is a multi-stakeholder stochastic system whose performance is the outcome of complex interactions between its multiple decisions-makers under a high degree of uncertainty. Inadequate understanding of uncertainty and stakeholder preferences leads to adverse effects including airline losses, delays and disruptions. This thesis studies a set of topics in airline scheduling and air traffic control to mitigate some of these issues. The first part of the thesis focuses on building aircraft schedules that are robust against delays. We develop a robust optimization approach for building aircraft routes. The goal is to mitigate propagated delays, which are defined as the delays caused by the late arrival of aircraft from earlier flights and are the top cause of flight delays in the United States air transportation system. The key feature of our model is that it allows us to handle correlation in flight delays explicitly that existing approaches cannot handle efficiently. We propose an efficient decomposition algorithm to solve the robust model and present the results of numerical experiments, based on data from a major U.S. airline, to demonstrate its effectiveness compared to existing approaches. The second part of the thesis focuses on improving the planning of air traffic flow management (ATFM) programs by incorporating airline preferences into the decision-making process. We develop a voting mechanism to gather airline preferences of candidate ATFM designs. A unique feature of this mechanism is that the candidates are drawn from a domain with infinite cardinality described by polyhedral sets. We conduct a detailed case study based on actual schedule data at San Francisco International Airport to assess its benefits in planning of ground delay programs. Finally, we study an integrated airline network planning model which incorporates passenger choice behavior. We model passenger demand using a multinomial logit choice model and integrate it into a fleet assignment and schedule design model. To tackle the formidable computational challenge associated with solving this model, we develop a reformulation, decomposition and approximation scheme. Using data from a major U.S. airline, we prove that the proposed approach brings significant profit improvements over existing methods.
A concise resource to the best practices and problem-solving ideas for understanding the airline network planning and scheduling process Airline Network Planning and Scheduling offers a comprehensive resource that is filled with the industry's best practices that can help to inform decision-modeling and the problem-solving process. Written by two industry experts, the book is designed to be an accessible guide that contains information for addressing complex challenges, problems, and approaches that arise on the job. The chapters begin by addressing the complex topics at a broad, conceptual level before moving on to more detailed modeling in later chapters. This approach follows the standard airline planning process and reflects the duties of the day-to-day job of network/schedule planners. To help gain a practical understanding of the information presented, each chapter includes exercises and data based on real-world case studies. In addition, throughout the book there are graphs and illustrations as well as, information on the most recent advances in airline network and planning research. This important resource: Takes a practical approach when detailing airline network planning and scheduling practices as opposed to a theoretical perspective Puts the focus on the complexity and main challenges as well as current practices and approaches to problem-solving and decision-making Presents the information in a logical sequence that begins with broad, conceptual topics and gradually delves into more advanced topics that address modeling Contains international standard airline planning processes, the day-to-day responsibilities of the job, and outlines the steps taken when building an airline network and schedule Includes numerous case studies, exercises, graphs, and illustrations throughout Written for professionals and academics, Airline Network Planning and Scheduling offers a resource for understanding best practices and models as well as the challenges involved with network planning and scheduling.
We study strategic and operational measures of improving airline system performance and reducing delays for aircraft, crew and passengers. As a strategic approach, we study robust optimization models, which capture possible future operational uncertainties at the planning stage, in order to generate solutions that when implemented, are less likely to be disrupted, or incur lower costs of recovery when disrupted. We complement strategic measures with operational measures of managing delays and disruptions by integrating two areas of airline operations thus far separate - disruption management and flight planning. We study different classes of models to generate robust airline scheduling solutions. In particular, we study, two general classes of robust models: (i) extreme-value robust-optimization based and (ii) chance-constrained probability-based; and one tailored model, which uses domain knowledge to guide the solution process. We focus on the aircraft routing problem, a step of the airline scheduling process. We first show how the general models can be applied to the aircraft routing problem by incorporating domain knowledge. To overcome limitations of solution tractability and solution performance, we present budget-based extensions to the general model classes, called the Delta model and the Extended Chance-Constrained programming model. Our models enhance tractability by reducing the need to iterate and re-solve the models, and generate solutions that are consistently robust (compared to the basic models) according to our performance metrics. In addition, tailored approaches to robustness can be expressed as special cases of these generalizable models. The extended models, and insights gleaned, apply not only to the aircraft routing model but also to the broad class of large-scale, network-based, resource allocation. We show how our results generalize to resource allocation problems in other domains, by applying these models to pharmaceutical supply chain and corporate portfolio applications in collaboration with IBM's Zurich Research Laboratory. Through empirical studies, we show that the effectiveness of a robust approach for an application is dependent on the interaction between (i) the robust approach, (ii) the data instance and (iii) the decision-maker's and stakeholders' metrics. We characterize the effectiveness of the extreme-value models and probabilistic models based on the underlying data distributions and performance metrics. We also show how knowledge of the underlying data distributions can indicate ways of tailoring model parameters to generate more robust solutions according to the specified performance metrics. As an operational approach towards managing airline delays, we integrate flight planning with disruption management. We focus on two aspects of flight planning: (i) flight speed changes; and (ii) intentional flight departure holds, or delays, with the goal of optimizing the trade-off between fuel costs and passenger delay costs. We provide an overview of the state of the practice via dialogue with multiple airlines and show how greater flexibility in disruption management is possible through integration. We present models for aircraft and passenger recovery combined with flight planning, and models for approximate aircraft and passenger recovery combined with flight planning. Our computational experiments on data provided by a European airline show that decrease in passenger disruptions on the order of 47.2%-53.3% can be obtained using our approaches. We also discuss the relative benefits of the two mechanisms studied - that of flight speed changes, and that of intentionally holding flight departures, and show significant synergies in applying these mechanisms. We also show that as more information about delays and disruptions in the system is captured in our models, further cost savings and reductions in passenger delays are obtained.
Thisvolumecontainsaselectionofpapersfromthe5thInternationalConference on the Practice and Theory of Automated Timetabling (PATAT 2004) held in Pittsburgh, USA, August 18-20, 2004. Indeed, as we write this preface, in the Summer of 2005, we note that we are about one month away from the tenth anniversary of the very?rst PATAT conference in Edinburgh. Since those very early days, the conference series has gone from strength to strength and this volume represents the latest in a series of?ve rigorously refereed volumes which showcase a broad spectrum of ground-breaking timetabling research across a very wide range of timetabling problems and applications. Timetabling is an area that unites a number of disparate?elds and which cuts across a number of diverse academic disciplines. While the most obvious instances of timetabling occur in educational institutions, timetabling also - pears in sports applications, transportation planning, project scheduling, and many other?elds. Viewing timetabling as a unifying theme enables researchers fromthesevariousareastolearnfromeachotherandtoextendtheirown- searchandpracticeinnewandinnovativeways. Thisvolumecontinuesthetrend of the conference series to extend the de?nition of timetabling beyond its edu- tional roots. In this volume, seven of the 19 papers involve domains other than education. Of course, educationaltimetabling remains at the coreof timetabling research, and the papers in this volume represent the full range of this area including exam timetabling, room scheduling, and class rostering.
This book contains selected papers from the symposium "Operations Research 2010" which was held from September 1-3, 2010 at the "Universität der Bundeswehr München", Germany. The international conference, which also serves as the annual meeting of the German Operations Research Society (GOR), attracted more than 600 participants from more than thirty countries. The general theme "Mastering Complexity" focusses on a natural component of the globalization process. Financial markets, traffic systems, network topologies and, last but not least, energy resource management, all contain complex behaviour and economic interdependencies which necessitate a scientific solution. Operations Research is one of the key instruments to model, simulate and analyze such systems. In the process of developing optimal solutions, suitable heuristics and efficient procedures are some of the challenges which are discussed in this volume.
Major airlines aim to generate schedules that maximize profit potential and satisfy constraints involving flight schedule design, fleet assignment, aircraft maintenance routing and crew scheduling. Almost all aircraft and crew schedule optimization models assume that flights, aircraft, crews, and passengers operate as planned. Thus, airlines typically construct plans that maximize revenue or minimize cost based on the assumption that every flight departs and arrives as planned. Because flight delays and cancellations result from numerous causes, including severe weather conditions, unexpected aircraft and crew failures, and congestion at the airport and in the airspace, this deterministic, optimistic scenario rarely, if ever, occurs. In fact, schedule plans are frequently disrupted and airlines often incur significant costs in addition to those originally planned. To address this issue, an approach is to design schedules that are robust to schedule disruptions and attempt to minimize realized, and not planned, costs. In this research, we review recovery approaches and robustness criteria in the context of airline schedule planning. We suggest new approaches for designing fleet assignments that facilitate recovery operations, and we present models to generate plans that allow for more robust crew operations, based on the idea of critical crew connections. We also examine the impact on robustness of new scheduling practices to debank hub airports.