Dissertations / Theses on the topic 'Airline Schedule'

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; and, (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, February 1999.
Includes bibliographical references (p. 83-86).
We consider the problem of integrating flight schedule design and fleet assignment decisions at airlines. The flight schedule design problem involves selecting and scheduling the set of flight legs that an airline will include in its service network. Fleet assignment involves assigning a particular aircraft type to each flight leg in the schedule. Due to the particularly challenging nature of schedule design problems, we limit our focus to that of incremental schedule design. Incremental schedule design involves the modification of a given flight schedule to produce an improved schedule by adding, deleting, and rescheduling flight legs. We present models and algorithms to achieve incremental schedule design and unlike previous schedule design efforts, we explicitly model flight demand and supply interactions. We present two case studies, using our models and algorithms. The first case study allows flight additions and deletions, while the second allows flights to be rescheduled. Future case studies well integrate these flight modification options. In our first case study, high-yield flights are maintained in the schedule and low-yield flights are dropped. Although the resulting schedule incurs higher spill costs, the savings from flight operating costs are sufficiently large to offset these higher spill costs, resulting in a more profitable schedule. The second case study, allowing flights to be rescheduled, considers several network sizes including the domestic network of a large U.S. airline. We consider Free Flight, a system allowing reduced flying times due to improved utilization of the national airspace. We find that reductions in flying times of about 10% can lead to dramatic cost savings for the airline, including reductions in the number of aircraft needed to fly the flight schedule.
by Manoj Lohatepanont.
S.M.

Airline schedule punctuality is a complex problem and one of the major concerns of the airline top management. Flight schedule disturbances may occur as delays and/or cancellations. There are many internal and external reasons for delays. These delays may propagate in the aircraft cycles and cause a large schedule disturbance. This may influences passenger satisfaction and airline resources. The objective of this research is to formulate a systematic approach for schedule punctuality which supports management decision making. The punctuality management system is structured to combine all schedule punctuality components, input and output variables. Five models are incorporated in this system. The first model is the disturbance model which generates random delays based on an estimated Lognormal delay distribution function. The delay analysis is carried out from a one year sample of delay statistics in which general, original , reactionary and other delay types are classified. The second model is the recovery model which incorporates the disturbance model with management strategies to determine delay propagation. A PC based simulation model (SKDMOD) is developed as a prototype which integrates disturbance and recovery models using SIMSCRIPT 11.5. 18 management strategies are simulated covering ground times (30, 40 and 50 minutes), maximum delay times to assign spare aircraft (1, 2, 3, 4, 5, and 6 hours) and spare aircraft using part of the domestic network of Saudi Arabia. The third model is the passengers' attitude model which determines the delay impact functions and the maximum passenger revenue loss based on 262 responses from a passenger interview survey. The fourth model is the revenue model which estimates the passengers' revenue loss. The fifth model is the cost model which estimates the extra cost resulting from implementation of the management strategies. All strategies are evaluated to determine the optimum based on profit and profit margin. OPTIM is the optimization program developed to find the optimum strategy(ies). This approach provides a guidelines for the management of punctuality. It integrates all the tools developed in a decision support system framework.

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.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2001.
Includes bibliographical references (p. 161-163).
Airline scheduling is a daunting task. Much time and resources are spent by airlines developing a schedule that meets expectations of profitability and competitiveness. Most of the time, however, the reliability aspect has a minor, if any, role in such a process. In reality disruption of the schedule occurs due to unforeseen events such as weather conditions, traffic congestion, and mechanical problems. The outcomes of these events are cancellations and delays. The impact that these disruptions have on airline operations is not only the increased cost for system maintenance and recovery, but also the loss of profitability and the perception of poor and unreliable service for the flying customer. In this thesis we present an analysis of the schedule design process, highlight the drawbacks of the current proceedings and outline of new and more flexible framework for schedule design. We define a reliability measure, the Option Value, and a way of comparing flights based on the reliability they are providing, via the Option Disruption Value. The idea of reliability is based on the concept of flight performance: a flight is more reliable if it is able to match or outperform the on-time performance of the flights that leaves its origin station and arrives at its final destination at or near its arrival and departure times. Based on these two measurements, we quantify the robustness and coverage of a sample schedule. Alternative passenger ratings are defined based on the concept of alternative itineraries (Coverage) and alternative independent itineraries (Robustness) that connect two locations. These are the Flight Options and the Flight Protection Options, respectively. Fifteen methods to modify flight schedule are proposed. One method, Reduce/increase Flight Slack Time (R/IFTS) was evaluated. Results indicate that R/IFTS was effective in increasing reliability in 70% of the flight considered, but that other methods need to be employed if reliability is to be increased further.
by Massimo Morin.
S.M.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1996.
Includes bibliographical references (p. 133-148).
by Rajesh Gopalakrishna Shenoi.
Ph.D.

In order to meet the fast-growing demand, airlines have applied much more compact air-fleet operation schedules which directly lead to airport congestion. One result is the flight delay, which appears more frequently and seriously; the flight delay can also significantly damage airline's profitability and reputation The aim of this project is to enhance the dispatch reliability of Australian X Airline's fleet through a newly developed approach to reliability modeling, which employs computer-aided numerical simulation of the departure delay distribution and related cost to achieve the flight schedule optimization. The reliability modeling approach developed in this project is based on the probability distributions and Monte Carlo Simulation (MCS) techniques. Initial (type I) delay and propagated (type II) delay are adopted as the criterion for data classification and analysis. The randomicity of type I delay occurrence and the internal relationship between type II delay and changed flight schedule are considered as the core factors in this new approach of reliability modeling, which compared to the conventional assessment methodologies, is proved to be more accurate on the departure delay and cost evaluation modeling. The Flight Delay and Cost Simulation Program (FDCSP) has been developed (Visual Basic 6.0) to perform the complicated numerical calculations through significant amount of pseudo-samples. FDCSP is also designed to provide convenience for varied applications in dispatch reliability modeling. The end-users can be airlines, airports and aviation authorities, etc. As a result, through this project, a 16.87% reduction in departure delay is estimated to be achieved by Australian X Airline. The air-fleet dispatch reliability has been enhanced to a higher level - 78.94% compared to initial 65.25%. Thus, 13.35% of system cost can be saved. At last, this project also achieves to set a more practical guideline for air-fleet database and management upon overall dispatch reliability optimization.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2003.
Includes bibliographical references (leaves 61-62).
Inclement weather at an airline's hub airport can be devastating to that airline's schedule. The repercussions resonate throughout the airline's network as capacity is reduced, connections are missed, and passengers are delayed on a larger scale than during irregular operations at a spoke airport. The main hypothesis behind the work presented in this thesis is that by shifting a small fraction of a connecting bank to strategically located, under-utilized airports during irregular operations, an airline can reduce costs and aircraft delays relative to current industry rescheduling practices. These proposed "virtual hubs" would, in addition to hosting selected connecting traffic that is shifted from the original hub in order to maximize passenger flow through the network, also reduce the demand on the nominal hub airport. The primary goal of this research project was to develop methods for the implementation of a virtual hub network and evaluate the potential benefits to the airline industry. To that end, a mathematical formulation is presented along with a case study of the benefits of a virtual hub to a major US airline. The actual recovered schedule and delay statistics for a day of irregular operations was compared to the results from the virtual hub network. Results indicate that significant passenger delays are reduced 94% and flight cancellations are reduced by 15% when a virtual hub network is implemented.
by Michelle J. Karow.
S.M.

Thesis (S.M. in Transportation)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 125-127).
Every airline passenger faces the risk of arriving late because flight times are subjected to many sources of variability. These can be weather conditions and airspace congestion, imbalances between airport demand and capacity, fleet and crew availability, technical failures and delays in maintenance, and other airline operations such as boarding and fueling. The main objective of this thesis is to explore the most common sources of variability in flight operations and study how U.S. carriers add buffer time (or pad) to scheduled block time to account for them. Using flight data from FAA Aviation System Performance Metrics, we analyze the scheduled and actual flight times on 2359 directional non-stop domestic routes during 2009. The time of each flight is decomposed to delay at gate, taxi-out time, airborne time and taxi-in time. Then, the buffer time of each flight is computed, using as nominal airborne time the lO percentile of the actual airborne time distribution. Our study consists of two parts. First, an aggregate statistical analysis is performed, concentrating on trends and correlations among factors such as buffer, flight time components, route distance, seasonality effects, delays caused by Ground Delay Programs, time of day and day of week, a flight's relative position to other flights operated on the same day by the same aircraft, total number of flights operated by the same aircraft during a day, the role of airport and carriers' network structure. Finally, we perform an econometric analysis through linear regression models to estimate how some of the above factors affect carriers' padding and their on-time performance. The results indicate distance and time of day to be the most important factors that affect schedule padding. While absolute buffer increases with distance, when buffer is measured as a fraction of nominal block time it decreases exponentially. Furthermore, buffer and on-time performance fluctuate strongly over the course of the day, with flights scheduled to arrive during the evening peak having the worst on-time performance, despite the fact that these flights are padded the most. The data reveal that among the studied carriers Southwest pads its schedule more extensively, achieving a very high on-time performance, whereas other low cost carriers pad their flights substantially less, and have a lower on-time performance. Our findings also show that flights destined to the carrier's hub have more buffer than flights destined to spoke airports. Last, competition has a positive effect on schedule buffer and on-time performance.
by Gerasimos Skaltsas.
S.M.in Transportation

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; and, (S.M.)--Massachusetts Institute of Technology, Operations Research Center, 2004.
Includes bibliographical references (p. 87-89).
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.
by Claudine Biova Agbokou.
S.M.

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.

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (leaf 51).
Airlines incur significant additional costs when bad weather at a hub-airport causes delays and cancellations throughout their entire network. One new recovery strategy called the virtual hub alleviates the effects of large delays at a hub-airport by setting up and diverting flight legs to nearby virtual-hub-airports. The effectiveness of the virtual hub strategy is tested through simulated days using the MIT Extensible Airport Network Simulator (MEANS). Increasingly complex heuristics are implemented to perform the virtual hub. Results indicate that the virtual hub recovery strategy can reduce the number of passengers going through the hub that get abandoned by 38.0%, the number going through the hub with significant delays by 30.4%, and the average flight leg delay of the airline can be reduced by 49.5%. The heuristics are able to produce effective solutions in a matter of a few minutes.
by Jason A. Loy.
M.Eng.

Thesis (Ph. D. in Transportation Studies)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 157-162).
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.
by Lavanya Marla.
Ph.D.in Transportation Studies

Air transportation offers both passenger and freight services that are essential for economic growth and development. In a highly competitive environment, airline companies have to control their operating costs by managing their flights, aircraft, and crews effectively. This motivates the extensive use of analytical techniques to solve complex problems related to airline operations planning, which includes schedule design, fleet assignment, aircraft routing, and crew scheduling. The initial problem addressed by airlines is that of schedule design, whereby a set of flights having specific origin and destination cities as well as departure and arrival times is determined. Then, a fleet assignment problem is solved to assign an aircraft type to each flight so as to maximize anticipated profits. This enables a decomposition of subsequent problems according to the different aircraft types belonging to a common family, for each of which an aircraft routing problem and a crew scheduling or pairing problem are solved. Here, in the aircraft routing problem, a flight sequence or route is built for each individual aircraft so as to cover each flight exactly once at a minimum cost while satisfying maintenance requirements. Finally, in the crew scheduling or pairing optimization problem, a minimum cost set of crew rotations or pairings is constructed such that every flight is assigned a qualified crew and that work rules and collective agreements are satisfied. In practice, most airline companies solve these problems in a sequential manner to plan their operations, although recently, an increasing effort is being made to develop novel approaches for integrating some of the airline operations planning problems while retaining tractability. This dissertation formulates and analyzes three different models, each of which examines a composition of certain pertinent airline operational planning problems. A comprehensive fourth model is also proposed, but is relegated for future research. In the first model, we integrate fleet assignment and schedule design by simultaneously considering optional flight legs to select along with the assignment of aircraft types to all scheduled legs. In addition, we consider itinerary-based demands pertaining to multiple fare-classes. A polyhedral analysis of the proposed mixed-integer programming model is used to derive several classes of valid inequalities for tightening its representation. Solution approaches are developed by applying Benders decomposition method to the resulting lifted model, and computational experiments are conducted using real data obtained from a major U.S. airline (United Airlines) to demonstrate the efficacy of the proposed procedures as well as the benefits of integration. A comparison of the experimental results obtained for the basic integrated model and for its different enhanced representations reveals that the best modeling strategy among those tested is the one that utilizes a variety of five types of valid inequalities for moderately sized problems, and further implements a Benders decomposition approach for relatively larger problems. In addition, when a heuristic sequential fixing step is incorporated within the algorithm for even larger sized problems, the computational results demonstrate a less than 2% deterioration in solution quality, while reducing the effort by about 21%. We also performed an experiment to assess the impact of integration by comparing the proposed integrated model with a sequential implementation in which the schedule design is implemented separately before the fleet assignment stage based on two alternative profit maximizing submodels. The results obtained demonstrate a clear advantage of utilizing the integrated model, yielding an 11.4% and 5.5% increase in profits in comparison with using the latter two sequential models, which translates to an increase in annual profits by about $28.3 million and $13.7 million, respectively. The second proposed model augments the first model with additional features such as flexible flight times (i.e., departure time-windows), schedule balance, and demand recapture considerations. Optional flight legs are incorporated to facilitate the construction of a profitable schedule by optimally selecting among such alternatives in concert with assigning the available aircraft fleet to all the scheduled legs. Moreover, network effects and realistic demand patterns are effectively represented by examining itinerary-based demands as well as multiple fare-classes. Allowing flexibility on the departure times of scheduled flight legs within the framework of an integrated model increases connection opportunities for passengers, hence yielding robust schedules while saving fleet assignment costs. A provision is also made for airlines to capture an adequate market share by balancing flight schedules throughout the day. Furthermore, demand recapture considerations are modeled to more realistically represent revenue realizations. For this proposed mixed-integer programming model, which integrates the schedule design and fleet assignment processes while considering flexible flight times, schedule balance, and recapture issues, along with optional legs, itinerary-based demands, and multiple fare-classes, we perform a polyhedral analysis and utilize the Reformulation-Linearization Technique in concert with suitable separation routines to generate valid inequalities for tightening the model representation. Effective solution approaches are designed by applying Benders decomposition method to the resulting tightened model, and computational results are presented to demonstrate the efficacy of the proposed procedures. Using real data obtained from United Airlines, when flight times were permitted to shift by up to 10 minutes, the estimated increase in profits was about $14.9M/year over the baseline case where only original flight legs were used. Also, the computational results indicated a 1.52% and 0.49% increase in profits, respectively, over the baseline case, while considering two levels of schedule balance restrictions, which can evidently also enhance market shares. In addition, we measured the effect of recaptured demand with respect to the parameter that penalizes switches in itineraries. Using values of the parameter that reflect 1, 50, 100, or 200 dollars per switched passenger, this yielded increases in recaptured demand that induced additional profits of 2.10%, 2.09%, 2.02%, and 1.92%, respectively, over the baseline case. Overall, the results obtained from the two schedule balance variants of the proposed integrated model that accommodate all the features of flight retiming, schedule balance, and demand recapture simultaneously, demonstrated a clear advantage by way of $35.1 and $31.8 million increases in annual profits, respectively, over the baseline case in which none of these additional features is considered. In the third model, we integrate the schedule design, fleet assignment, and aircraft maintenance routing decisions, while considering optional legs, itinerary-based demands, flexible flight retimings, recapture, and multiple fare-classes. Instead of utilizing the traditional time-space network (TSN), we formulate this model based on a flight network (FN) that provides greater flexibility in accommodating integrated operational considerations. In order to consider through-flights (i.e., a sequence of flight legs served by the same aircraft), we append a set of constraints that matches aircraft assignments on certain inbound legs into a station with that on appropriate outbound legs at the same station. Through-flights can generate greater revenue because passengers are willing to pay a premium for not having to change aircraft on connecting flights, thereby reducing the possibility of delays and missed baggage. In order to tighten the model representation and reduce its complexity, we apply the Reformulation-Linearization Technique (RLT) and also generate other classes of valid inequalities. In addition, since the model possesses many equivalent feasible solutions that can be obtained by simply reindexing the aircraft of the same type that depart from the same station, we introduce a set of suitable hierarchical symmetry-breaking constraints to enhance the model solvability by distinguishing among aircraft of the same type. For the resulting large-scale augmented model formulation, we design a Benders decomposition-based solution methodology and present extensive computational results to demonstrate the efficacy of the proposed approach. We explored four different algorithmic variants, among which the best performing procedure (Algorithm A1) adopted two sequential levels of Benders partitioning method. We then applied Algorithm A1 to perform several experiments to study the effects of different modeling features and algorithmic strategies. A summary of the results obtained is as follows. First, the case that accommodated both mandatory and optional through-flight leg pairs in the model based on their relative effects on demands and enhanced revenues achieved the most profitable strategy, with an estimated increase in expected annual profits of $2.4 million over the baseline case. Second, utilizing symmetry-breaking constraints in concert with compatible objective perturbation terms greatly enhanced problem solvability and thus promoted the detection of improved solutions, resulting in a $5.8 million increase in estimated annual profits over the baseline case. Third, in the experiment that considers recapture of spilled demand from primary itineraries to other compatible itineraries, the different penalty parameter values (100, 50, and 1 dollars per re-routed passenger) induced average respective proportions of 3.2%, 3.4%, and 3.7% in recaptured demand, resulting in additional estimated annual profits of $3.7 million, $3.8 million, and $4.0 million over the baseline case. Finally, incorporating the proposed valid inequalities within the model to tighten its representation helped reduce the computational effort by 11% on average, while achieving better solutions that yielded on average an increase in estimated annual profits of $1.4 million. In closing, we propose a fourth more comprehensive model in which the crew scheduling problem is additionally integrated with fleet assignment and aircraft routing. This integration is important for airlines because crew costs are the second largest component of airline operating expenses (after fuel costs), and the assignment and routing of aircraft plus the assignment of crews are two closely interacting components of the planning process. Since crews are qualified to typically serve a single aircraft family that is comprised of aircraft types having a common cockpit configuration and crew rating, the aircraft fleeting and routing decisions significantly impact the ensuing assignment of cockpit crews to flights. Therefore it is worthwhile to investigate new models and solution approaches for the integrated fleeting, aircraft routing, and crew scheduling problem, where all of these important inter-dependent processes are handled simultaneously, and where the model can directly accommodate various work rules such as imposing a specified minimum and maximum number of flying hours for crews on any given pairing, and a minimum number of departures at a given crew base for each fleet group. However, given that the crew scheduling problem itself is highly complex because of the restrictive work rules that must be heeded while constructing viable duties and pairings, the formulated integrated model would require further manipulation and enhancements along with the design of sophisticated algorithms to render it solvable. We therefore recommend this study for future research, and we hope that the modeling, analysis, and algorithmic development and implementation work performed in this dissertation will lend methodological insights into achieving further advances along these lines.
Ph. D.

The events of September 11,2001 followed by the oil price hike and the economic crisis of 2008, have lead to a drop in the demand for air travel. Airlines have attempted to return to profitability by cutting service in certain unattractive routes and airports. Simultaneously, delays and excess demand at a few major hubs have lead to airline introducing service at reliever airports. This dissertation attempts to capture the changes in the airline network by utilizing a supply-demand framework.
Ph. D.

Post deregulation, many U.S. airlines created hubs with banked schedules, however, in the past decade these same airlines began to experiment with depeaking their schedules to reduce costs and improve operational performance. To date there has been little research that has investigated revenue and operational shifts associated with depeaked schedules; yet understanding the trade-offs among revenue, costs, and operational performance at a network level is critical before airlines will consider future depeaking and related congestion-management strategies. This study develops data cleaning and analysis methodologies based on publicly available data that are used to quantify airport-level and network-level revenue and operational changes associated with schedule depeaking. These methodologies are applied to six case studies of airline depeaking over the past decade. Results show that depeaking is associated with revenue per available seat mile (RASM) increasing slower than the rest of the network and the industry as a whole. Depeaking is associated with improved operations for both the depeaking airlines and competitors. Airports benefit from increases in non-aeronautical sales associated with connecting passengers spending more time in the terminal. The underlying reasons driving airlines' scheduling decisions during depeaking vary greatly by case. Results from the study provide insights for airlines that are considering depeaking and the airports which are affected. The results suggest that losses in RASM and no improvement in operations could potentially lead an airline to repeak, and that RASM is prone to fall when a strong competitive threat exists.

Improving the predictability of airline schedules in the National Airspace System (NAS) has been a constant endeavor, particularly as system delays grow with ever-increasing demand. Airline schedules need to be resistant to perturbations in the system including Ground Delay Programs (GDPs) and inclement weather. The strategic search heuristic proposed in this dissertation significantly improves airline schedule reliability by assigning airport departure and arrival slots to each flight in the schedule across a network of airports. This is performed using a multi-objective optimization approach that is primarily based on historical flight and taxi times but also includes certain airline, airport, and FAA priorities. The intent of this algorithm is to produce a more reliable, robust schedule that operates in today's environment as well as tomorrow's 4-Dimensional Trajectory Controlled system as described the FAA's Next Generation ATM system (NextGen). This novel airline schedule optimization approach is implemented using a multi-objective evolutionary algorithm which is capable of incorporating limited airport capacities. The core of the fitness function is an extensive database of historic operating times for flight and ground operations collected over a two year period based on ASDI and BTS data. Empirical distributions based on this data reflect the probability that flights encounter various flight and taxi times. The fitness function also adds the ability to define priorities for certain flights based on aircraft size, flight time, and airline usage. The algorithm is applied to airline schedules for two primary US airports: Chicago O'Hare and Atlanta Hartsfield-Jackson. The effects of this multi-objective schedule optimization are evaluated in a variety of scenarios including periods of high, medium, and low demand. The schedules generated by the optimization algorithm were evaluated using a simple queuing simulation model implemented in AnyLogic. The scenarios were simulated in AnyLogic using two basic setups: (1) using modes of flight and taxi times that reflect highly predictable 4-Dimensional Trajectory Control operations and (2) using full distributions of flight and taxi times reflecting current day operations. The simulation analysis showed significant improvements in reliability as measured by the mean square difference (MSD) of filed versus simulated flight arrival and departure times. Arrivals showed the most consistent improvements of up to 80% in on-time performance (OTP). Departures showed reduced overall improvements, particularly when the optimization was performed without the consideration of airport capacity. The 4-Dimensional Trajectory Control environment more than doubled the on-time performance of departures over the current day, more chaotic scenarios. This research shows that airline schedule reliability can be significantly improved over a network of airports using historical flight and taxi time data. It also provides for a mechanism to prioritize flights based on various airline, airport, and ATC goals. The algorithm is shown to work in today's environment as well as tomorrow's NextGen 4-Dimensional Trajectory Control setup.
Ph.D.
Department of Industrial Engineering and Management Systems
Engineering and Computer Science
Industrial Engineering PhD

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2003.
Includes bibliographical references (leaves 135-136).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
This thesis examines how to model the choice of individual travelers among various possible travel alternatives in the airline industry. A review of the models used to represent that choice situation in the Passenger Origin-Destination Simulator (PODS) was undertaken for two reasons. First, the development of computational capabilities has lead to advancements in consumer choice theory that enabled the implementation of more flexible models like mixed logit models. Second, the increasing competition of low-cost new entrant airlines has put great pressure on pricing practices of traditional network carriers. This increasing competition has also compelled these carriers to focus on their strengths, for example, schedule coverage. In this thesis, after a comparison between the PODS Passenger Choice Model and the literature on consumer choice theory, we will then focus on how to model passenger preference for schedule. The review of the literature on air traveler choice reveals that most authors have used discrete choice models, like standard logit or nested logit models, to represent the choice of individual passengers among travel alternatives. However, the logit model has two limitations in the air traveler choice problem: it can accommodate neither random taste variation in some elements of the passenger utility function nor the complex substitution patterns across travel alternatives modeled in PODS. However, we show that the highly flexible mixed logit model brings a solution to these limitations and the choice process modeled in PODS can be approximated by a set of mixed logit models. In the second part of the thesis, we focus on how passenger preference for schedule is modeled in PODS. In the current model, a constant replanning disutility is added to the cost of all paths that are not convenient to the passenger. However, the current approach does not differentiate among paths based on their level of schedule inconvenience and this leads to distortions in the valuation of the revenue advantage of the carrier offering the best schedule. We propose in this thesis an alternative approach called the variable replanning disutility model. In this model, the replanning disutility added to the cost of paths depends on the time location of the path and its level of schedule inconvenience. PODS simulation results show that the variable replanning disutility model leads to a more realistic valuation of the revenue advantage associated with a better schedule coverage.
by Emmanuel Carrier.
S.M.

Thesis (S.M. in Transportation)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; and, (S.M.)--Massachusetts Institute of Technology, Technology and Policy Program, 2004.
Includes bibliographical references (p. 99-100).
The purpose of the National Airspace System Strategy Simulator is to provide the FAA with a decision support system to evaluate long-term infrastructure and regulatory strategies. The NAS strategy simulator consists of several modules representing the different entities within the NAS embedded in a system dynamics framework. The MIT Airline Scheduling Module is the module within the NAS Strategy Simulator that represents the decision making process of the airlines with respect to the schedules that they fly. The MIT Airline Scheduling Module is an incremental optimization tool to determine schedule changes from one time step to another that best meets demand using available resources. The optimization model combines an Integrated Schedule Design and Fleet Assignment model and a model, based on Passenger Decision Window model, that determines passenger preference for itineraries. We simultaneously establish frequency, departure times, fleet assignment, passenger loads and revenue within a competitive environment. Optimization methods often lead to extreme schedule decisions such as eliminating service to markets, often small markets, that are not financially profitable for the airlines. This is of grave concern to government policy makers as rural access to markets, goods and services is a politically charged subject. The issue is to understand what is likely to happen in small communities if the government doesn't respond in some way and how much subsidy, if any, would it be necessary to encourage airlines to maintain service in these markets. The approach we will use is based on economic policy and cost-benefit analysis.
by Flora A. Garcia.
S.M.
S.M.in Transportation

The airline industry has become more competitive since the introduction of low fare airlines in the 1990s. Thus, requirement of optimized planning is nowadays essential in order to obtain high market shares. The planning behind an airworthy traffic program is complex and includes many different resources, as fleet, crew and maintenance, which have to be synchronized. With a constant risk of unforeseen disruptions and variances in the weather conditions, robustness in form of time buffers are necessary in order to give the system a chance of recovery. Generally, one delay often affects several flights due to the lack of time buffers. In order to achieve cost reductions and to maximize profit, airlines tends to create traffic programs maximizing airborne hours. This report culminates in a conclusion of where and when time buffers, in a cost efficient way, can be added in Scandinavian Airline´s traffic program in order to obtain a higher robustness. This is performed by analyzing historical flight data and by implementing a Monte Carlo simulation.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics; in conjunction with the Leaders for Manufacturing Program at MIT, 2003.
Includes bibliographical references (p. 79-80).
The airline industry downturn that began in early 2000 was exacerbated not only by the terrorist attacks in the United States on September 11, 2001, but also by other pressures for strategic change. Continued growth and competition of low cost carriers coupled with changing purchasing habits of passengers have led industry analysts, airline executives, and investors alike, to question the continued viability of the traditional hub and spoke airline strategy. The financial success of Southwest Airlines and other low cost carriers is partly attributable to its high levels of employee productivity and equipment utilization. In April 2002, American Airlines made a step toward emulating this facet of Southwest's strategy by depeaking its flight schedule at Chicago's O'Hare International Airport. American's schedule change was analyzed, and the decision was evaluated from the market share, operational reliability, and cost perspectives. Average connection times increased by 6 minutes, and the average number of connections per arriving flight decreased by 2. Computer Reservation System market share data implied a market share neutral decision. Department of Transportation on-time performance data implied an improvement in reliability. Finally, the reduction in degree of schedule peaking implied a potential cost improvement through increased equipment utilization, lower required staffing levels, and improved employee productivity.
by Laura Lynn Bogusch.
S.M.

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (p. 59).
This project is to solve the fleet scheduling problem for a large airline. This requires matching each fight in a schedule to a plane in the fleet that will fly the scheduled flight on a particular day. This thesis shows how to use the network flow model of a flight schedule in order to find the smallest fleet required to fly a schedule without dropping any flights. This is done by pruning the whole network to a smaller set of more relevant arcs. It is also the goal of the project to perform these tasks without requiring a large powerful computer or workstation.
by Damon Marcus Lewis.
M.Eng.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2009.
Includes bibliographical references (p. 141-143).
In this thesis, we develop a framework for analyzing airlines' operational performances under different strategic decisions. A detailed study is conducted to compare differences between a major U.S. legacy carrier and a major U.S. low-cost carrier in terms of their scheduling practices, flight-based delays and on-time performances, network operations and mix of passengers, as well as passenger delays and disruptions. One major contribution of this thesis is that the framework we develop to evaluate airlines' performances is not restricted by the availability of proprietary airline data and can be adopted to estimate itinerary-based passenger demand for any U.S. airline included in the Bureau of Transportation Statistics database. Moreover, in this thesis, we compute delay for local and connecting passengers and provide a powerful tool for scenario analysis. Further, we: (1) identify root causes of delays as well as the impact throughout the network; (2) gain insights about how passenger delay can be reduced with different scheduling practices; and (3) guide the design of on-time performance metrics. Differences in scheduling practices, network operations, passenger mix, aircraft delays, and passenger delays between different airlines arise from carrier-specific characteristics. These characteristics should be considered when designing on-time performance metrics.
(cont.) Characteristics specific to the legacy carrier are: (1) its hubs experience heavy traffic volume and are often subject to ground delay programs (GDPs) caused by poor weather conditions; and (2) it operates banked hubs where a set of arriving flight legs are scheduled closely with a set of departing flight legs to allow passenger connections between arriving and departing flight legs. Characteristics specific to the low-cost carrier are: (1) it tends to fly into locations that are less impacted by weather conditions and less frequently subjected to GDPs; (2) passenger traffic is distributed more evenly in the system, unlike the case of the legacy carrier in which a much larger portion of passengers connect through the major hubs; and (3) it has depeaked schedules at the major airports which allow the carrier to achieve higher efficiency in turning aircraft. Another important distinction between the two airlines that needs to be considered when designing on-time performance metrics is: the ratio of passenger delay (especially disrupted passenger delay) to operated flight delay for the low-cost carrier was higher than the corresponding value for the legacy carrier. This difference indicates that flight-specific on-time performance metrics that ignore airline heterogeneity can be an inaccurate measure of passenger experiences. In this thesis, scenario representations pertaining to various levels of airport traffic under different weather conditions are analyzed within our framework. We measure passenger delays (that is, the positive difference between the actual arrival time of the passenger at his/her destination and the scheduled arrival time) and passenger disruptions, with a passenger disruption defined as a passenger who is re-booked on an itinerary other than that planned due to a missed connection or flight cancellation.
(cont.) Our results show that for the legacy carrier, an increase in flight operations of one percent on the "high-delay" day translates to an increase in the percentage of disrupted passengers (average disrupted passenger delay) of 22.2% (3.1%); for the low-cost carrier, an increase in flight operations of one percent only increases the percentage of disrupted passengers (average disrupted passenger delay) by 12.3% (2.7%). The above statistics suggest that under poor weather conditions, increasing flight operations at busy airports, compared to non-congested airports, can cause a much greater increase in passenger delay and disruptions when airport capacity is reduced by adverse weather condition.
by Ying Zhu.
S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006.
Includes bibliographical references (p. 87-90).
This thesis presents an approach for predicting operational performance of airlines on the basis of flight schedules and aircraft assignments. The methodology uses aggregate measures of properties of aircraft assignments, called Aircraft Assignment Key Performance Indicators (KPIs), and aims to find correlations between them and the operational performance of the airline. A simulation experiment is prepared to gather a large set of data points for analysis. A motivation is given for the use of control theoretic approaches in airline operations to utilize the KPIs as a basis for initial planning and corrective actions.
by Robin Riedel.
S.M.

The United States leads the world with more than sixty-one million take-offs and landings annually. Air Traffic Controllers are responsible for nearly sixty-four million domestic operations annually (ALPA, 2007). The majority of incursions, about 60%, occur due to pilot deviation, 20% due to operational error, and vehicle and pedestrian deviation account for the remainder (Young, 2001). With increasing air traffic, runway incursions have been proportionally on the rise. As traffic volume increases, the likelihood of a runway incursion increases. Whereas airport size remains constant, the number of landings and take-offs steadily increases. In this thesis, we focus on ways to mitigate congestion at the airports. The main reason for congestion is due to the widening gap between demand and capacity. We can decrease congestion either by enhancing capacity or by managing demand. Increasing capacity is considered as expensive and beyond our reach because of the following reasons (a) it requires large amounts of land which many airports do not own, (b) a huge amount of money is required for construction, and (c) the amount of time for the entire process of adding the new runway can mean a decade (Harsha, 2009). Therefore, we focus on managing demand. The main concern is to close the gap between demand and capacity by allocating scarce resources (i.e., runway usages) efficiently. This can be referred to as a scarce resource allocation problem, as resource is runway time slots. We will focus mainly on two types of efficient runway allocation techniques: airport combinatorial auctions and runway stable matching. In airport combinational auctions, well-designed auctions take place between airport and airlines for runways slots. Airport managers on behalf of airports act as auctioneers and airlines act as bidders. We propose an application which can implement combinatorial auctions among airlines. In runway stable matching, we propose application of Gale-Shapley algorithm and java based application for finding stable matching between airlines and available airports runway slots based on airline and airport's preferences.

碩士
淡江大學
土木工程研究所
83
When facing the problem of airline schedule perturba- tion management, the dispatcher is always dealing with random and fuzzy information. This paper thus applies fuzzy number to describe the uncertainties, including dwell and flying time. To simulate the information transmission process between technical and management divisions. We employ Wonneberger's invertible mapping between probability and possibility distribution to infer the arrival and departure time probability distributions of interconnected flights. Therefore, The stochastic model could be incorporated by these derived information. Based on different delay control strategies, we can search a set of reasonable alternatives. Meanwhile, two index of fuzzy inferences, possibility and necessity of achieving the management objective, are used to evaluate these strategies. Finally, there are several conclusions can be drawn as follows: 1.The application of interval fuzzy information has advantages on twofolds.It can not only keep the uncertainty characteristics of inter-actived airline schedule but also reduce the computation effort in calculating the expected cost function with probability distribution. 2.Using Wonneberger's invertible mapping under the situa- tion without full information, we can transform the uncertain information from either fuzzy or random form into the another.However,their inferences yield different results. 3.A set of alternatives can be obtained by different delay control strategies. These alternatives with different attributes, however, can be distinguished by fuzzy similarity ranking method. 4.After trading off these conflicted attributes, the best alternative named "Alternative 4" is close to the objective of decision maker, which is superior to any other alternative even with the minimum response cost.

碩士
雲林科技大學
全球運籌管理研究所碩士班
97
As science and technology excessive developed and high industrialization, that leads the carbon dioxide emission more and more recent year. Greenhouse effects and global warming are aggravated. Therefore the United Nations establish relevant regulations to restrain the gas of greenhouse emissions. On the other hand, international aviation industry grows quickly. Although the greenhouse gas emissions from aircraft only a small percent of all man made emissions, aircraft delivered directly into the atmosphere. In this research, we expect to achieve the greenhouse gas emissions minimum and the profit maximum. So we build a multi-objective model and use genetic algorithms to solve this problem. It also could obtain an optimum flight schedule. The results indicate this research could make the profit more then actual situation and reduced the greenhouse gas emissions effective, and obtain an optimum flught schedule.

博士
國立成功大學
交通管理學系碩博士班
97
This study develops a method of inequality-based multiobjective genetic algorithm (MMGA) to generate efficiently a time-effective multi-fleet aircraft routing in response to the schedule disruption of short-haul, quick turnaround flights, and attempts to optimize objective functions involving ground turnaround time, flight connection, flight swap, total flight delay time and flights over 15-minute delay of original schedules. The MMGA approach, which combines a traditional genetic algorithm with a multiobjective optimization method, can deal with multiple objectives in the same time, and then explores the optimal solution. The airline schedule disruption management problem is traditionally solved by mathematical modeling techniques that always require a precise mathematical model. However, airline operations involve too many factors that must be considered dynamically, making a precise mathematical model will be very difficult to define in time. Empirical analyses based on the real-world airline flight schedules demonstrate that the proposed method, method of inequality-based multiobjective genetic algorithm for airline schedule disruption management, can recover the perturbation efficiently within a very short time. Our results demonstrate that the application can yield high quality solutions in a few minutes and, consequently, can be employed as a real-time decision supporting tool for practical complex airline operations to save operation cost; increase passengers’ convenience and prevent air pollution.

This thesis reports on research into the applicability of intelligent agents in the airline scheduling environment. The methodology employed was to look at intelligent agent research and then, based on this, to build models that can be used to solve some of the airline scheduling problems. The following was done: · An agent-based model was developed that can assist airline schedulers in the maintenance of a disrupted schedule. The agent model consists of a hybrid approach combining elements of machine learning and expert systems. · A multiagent model was developed that can generate a profitable and flyable schedule. The multiagent model developed extends the traditional control structures of the hierarchical agent organisation to a matrix structure. This new model can be extended to any problem domain that deals with resource allocation and capacity management. To guide the thinking behind this research, a few questions were posed regarding the problem to be solved: Q1. Can intelligent agents play a role in the airline industry, with specific focus on the scheduling creation and maintenance process? Q2. What will the design of the agent models be if the scheduling needs of an airline have to be addressed? Q3. If the models as envisioned in question 2 can be created, what will the practical implications be? At a conceptual level the research produced three results: R1. No references were found to multiagent technology in the production or maintenance of airline schedules. This theoretical research into agent systems shows that there is applicability in the scheduling environment, with specific reference to schedule maintenance and generation. R2. An agent model was created that combines declarative knowledge with empirical learning to assist human schedulers in the day-to-day maintenanceof the schedule. Multiple solutions to a scheduling problem are generated by the agent using embedded scheduling rules. The agent then uses the Qlearning algorithm to learn the preferences of the human scheduler. This approach combines the best of expert systems and machine learning. To solve the problem of schedule generation, a multiagent system with a matrix governance model was introduced. Aircraft and airports were modelled as buying and selling agents. The business manager agent that assigns individual aircrafts to specific routes was defined. This was accomplished by matching individual aircraft capacity to origin-destination demand. The agent model was then expanded to show how the inclusion of a resource manager agent can handle system capacity management. This is a matrix governance model, as an aircraft agent is managed by a business manager agent, as well as by a resource manager agent. The initial results from the prototype show that this model can generate profitable and flyable schedules. The multiagent model developed extends the traditional hierarchical agent organisation to that of a matrix structure. The contract net protocol used for typical multiagent coordination was adapted to work in this new control structure. This new model can be extended to any problem domain that deals with resource allocation and capacity management. R3. A few airlines use expert systems to handle schedule disruptions. By introducing machine learning, a flexibility is achieved that is currently not available. The approach proposed for schedule generation is not guaranteed to provide optimal results like traditional operations research techniques, but it is useful for high-level analysis, long-term planning, new hub or alliance planning and research. It also has potential as a catalyst for integrated planning. Keywords: Multiagent systems, airline scheduling
Ehlers, E.M., Prof.