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

Multi-fidelity modelling approach for airline disruption management using simulation

Disruption to airline schedules is a key issue for the industry. There are various causes for disruption, ranging from weather events through to technical problems grounding aircraft. Delays can quickly propagate through a schedule, leading to high financial and reputational costs. Mitigating the impact of a disruption by adjusting the schedule is a high priority for the airlines. The problem involves rearranging aircraft, crew and passengers, often with large fleets and many uncertain elements. The multiple objectives, cost, delay and minimising schedule alterations, create a trade-off. In addition, the new schedule should be achievable without over-promising. This thesis considers the rescheduling of aircraft, the Aircraft Recovery Problem. The Aircraft Recovery Problem is well studied, though the literature mostly focusses on deterministic approaches, capable of modelling the complexity of the industry but with limited ability to capture the inherent uncertainty. Simulation offers a natural modelling framework, handling both the complexity and variability. However, the combinatorial aircraft allocation constraints are difficult for many simulation optimisation approaches, suggesting that a more tailored approach is required. This thesis proposes a two-stage multi-fidelity modelling approach, combining a low-fidelity Integer Program and a simulation. The deterministic Integer Program allocates aircraft to flights and gives an initial estimate of the delay of each flight. By solving in a multi-objective manner, it can quickly produce a set of promising solutions representing different trade-offs between disruption costs, total delay and the number of schedule alterations. The simulation is used to evaluate the candidate solutions and look for further local improvement. The aircraft allocation is fixed whilst a local search is performed over the flight delays, a continuous valued problem, aiming reduce costs. This is done by developing an adapted version of STRONG, a stochastic trust-region approach. The extension incorporates experimental design principles and projected gradient steps into STRONG to enable it to handle bound constraints. This method is demonstrated and evaluated with computational experiments on a set of disruptions with different fleet sizes and different numbers of disrupted aircraft. The results suggest that this multi-fidelity combination can produce good solutions to the Aircraft Recovery Problem. A more theoretical treatment of the extended trust-region simulation optimisation is also presented. The conditions under which a guarantee of the algorithm's asymptotic performance may be possible and a framework for proving these guarantees is presented. Some of the work towards this is discussed and we highlight where further work is required. This multi-fidelity approach could be used to implement a simulation-based decision support system for real-time disruption handling. The use of simulation for operational decisions raises the issue of how to evaluate a simulation-based tool and its predictions. It is argued that this is not a straightforward question of the real-world result being good or bad, as natural system variability can mask the results. This problem is formalised and a method is proposed for detecting systematic errors that could lead to poor decision making. The method is based on the Probability Integral Transformation using the simulation Empirical Cumulative Distribution Function and goodness of fit hypothesis tests for uniformity. This method is tested by applying it to the airline disruption problem previously discussed. Another simulation acts as a proxy real world, which deviates from the simulation in the runway service times. The results suggest that the method has high power when the deviations have a high impact on the performance measure of interest (more than 20%), but low power when the impact is less than 5%

Building Reliable Air-Travel Infrastructure Using Empirical Data and Stochastic Models of Airline Networks

Flight delays have been a growing issue and they have reached an all-time high in recent years, with the airlines’ on-time performance at its worst level in 2007 since 1995. The goal of this paper is to build stochastic models of airline networks and utilize publicly available data to answer the following policy questions: Which are the bottleneck airports in the US air-travel infrastructure? How would increasing airport capacity at these airports alleviate delay propagation? What are the appropriate metrics for measuring the robustness of airline schedules? How could these schedules be made more robust? Which flight in an aircraft rotation is a bottleneck flight? The contribution of this paper is two-fold. First, we develop stochastic models, using empirical data, to analyze the propagation of delays through air-transportation networks. Second, our analysis enables us to make policy recommendations regarding managing bottleneck resources in the air-travel infrastructure

Robust Decision-Support Tools for Airport Surface Traffic

Forecasts of departure demand are one of the driving inputs to tactical decision-support tools (DSTs) for airport surface traffic. While there are well-known results on average- or worst-case forecast uncertainty, it is the forecast errors which occur under best-case minimum-uncertainty conditions which constrain robust DST design and the achievable traffic benefits. These best-case errors have never previously been characterized. Several quantitative models and techniques for computing pushback forecasts are developed. These are tested against a dataset of 17,344 real-world airline ground operations covering 3 months of Lufthansa fights transiting Frankfurt International Airport. The Lufthansa dataset includes detailed timing information on all of the turn processes, including deboarding, catering, cleaning, fueling and boarding. The dataset is carefully filtered to obtain a sample of 3820 minimum-uncertainty ground events. The forecast models and techniques are tested against this sample, and it is observed that current pushback forecast errors (on the order of §15min) cannot be reduced by a factor of more than 2 or 3. Furthermore, for each ground event, only 3 observations are necessary to achieve this best-case performance: the available ground-time between actual onblock and scheduled offblock; the time until deboarding begins; and the time until boarding ends. Any DST used in real-world operations must be robust to this “noise floor". To support the development of robust DSTs, a unified framework called ceno-scale modeling is developed. This class of models encodes a wide range of observed delay mechanisms using multi-resource synchronization (MRS) feedback networks. A ceno-scale model instance is created for Newark International Airport, and the parameter sensitivity and model fidelity are tested against a detailed real-world dataset. Based on the validated model framework, several robust dual control strategies are proposed for airport surface traffic

Robust decision-support tools for airport surface traffic

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 107-113).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Forecasts of departure demand are one of the driving inputs to tactical decision-support tools (DSTs) for airport surface traffic. While there are well-known results on average- or worst-case forecast uncertainty, it is the forecast errors which occur under best-case minimum-uncertainty conditions which constrain robust DST design and the achievable traffic benefits. These best-case errors have never previously been characterized. Several quantitative models and techniques for computing pushback forecasts are developed. These are tested against a dataset of 17,344 real-world airline ground operations covering 3 months of Lufthansa flights transiting Frankfurt International Airport. The Lufthansa dataset includes detailed timing information on all of the turn processes, including deboarding, catering, cleaning, fueling and boarding. The dataset is carefully filtered to obtain a sample of 3820 minimum-uncertainty ground events. The forecast models and techniques are tested against this sample, and it is observed that current pushback forecast errors (on the order of ±15min) cannot be reduced by a factor of more than 2 or 3. Furthermore, for each ground event, only 3 observations are necessary to achieve this best-case performance: the available ground-time between actual onblock and scheduled offblock; the time until deboarding begins; and the time until boarding ends. Any DST used in real-world operations must be robust to this "noise floor". To support the development of robust DSTs, a unified framework called ceno-scale modelling is developed.(cont.) This class of models encodes a wide range of observed delay mechanisms using multi-resource synchronization (MRS) feedback networks. A ceno-scale model instance is created for Newark International Airport, and the parameter sensitivity and model fidelity are tested against a detailed real-world dataset. Based on the validated model framework, several robust dual control strategies are proposed for airport surface traffic.by Francis R. Carr.Ph.D

Dissertation - Preemptive Rerouting of Airline Passengers Under Uncertain Delays

An airline\u27s operational disruptions can lead to flight delays that in turn impact passengers, not only through the delay itself but also through possible missed connections. Much research has been done on crew recovery (rescheduling crews after a flight delay or cancellation), but little research has been done on passenger reaccommodation. Our goal is to design ways that passenger reaccommodation can be improved so that passengers can spend less time delayed and miss fewer connections. Since the length of a delay is often not known in advance, we consider preemptive rerouting of airline passengers before the length of the delay is known. Our goal is to reaccommodate passengers proactively as soon as it is known that a flight will be delayed instead of waiting until passengers have missed connections and to use known probabilities for the length of delay. In addition, we consider all of the affected passengers together so that we can effectively handle passengers\u27 competition for available seats. We can give certain seats to people with short connections or those connecting to international flights. When there is one delayed flight, we model the problem as a two-stage stochastic programming problem, with first-stage decisions that assign passengers initial itineraries and second-stage decisions that re-assign any passengers who are subsequently disrupted by the delay. We present a Benders decomposition approach to solving this problem. Computational results for this model are given, showing its effectiveness for reducing the length of passenger delays. When there is more than one delayed flight, we define a portfolio model which assigns passengers to portfolios that define their itineraries under all possible disruption outcomes. We focus on computational methods for solving this model

Impacts of revenue management on estimates of spilled passenger demand

Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 138-140).In the airline industry, spill refers to passenger demand turned away from a flight because demand has exceeded capacity. The accurate estimation of spill and the lost revenue it implies is an important parameter in airline fleet assignment models, where improved estimates lead to more profitable assignments. Previous models for spill estimation did not take into account the effects of passenger choice and airline revenue management. Since revenue management systems protect seats for later-arriving higher fare passengers, revenue management controls will influence the number of spilled passengers and their value because they will restrict availability to lower fare passengers even if seats on the aircraft are available. This thesis examines the effect of various revenue management systems and fare structures on spill, and, in turn, the marginal value of incremental capacity. The Passenger Origin Destination Simulator is used to simulate realistic passenger booking scenarios and to measure the value of spilled demand. A major finding of the research is that in less restricted fare structures and with traditional revenue management systems, increasing capacity on a flight leads to buy-down which can result in negative marginal revenues and therefore revenue losses. This behavior is contrary to conventional wisdom and is not considered in existing spill models. On the other hand, marginal revenues at low capacities are greater than would be predicted by first-choice-only spill models because some passengers will sell-up to higher fares to avoid spilling out. Additionally, because of passenger recapture between flights, adding capacity to one flight can lead to revenue losses on another. Therefore, the marginal value of incremental capacity is not always positive. Negative marginal revenues and associated revenue losses with increasing capacity can at least be partially mitigated by using more advanced revenue management forecasting and optimization algorithms which take into account passenger willingness to pay. The thesis also develops a heuristic analytical method for estimating spill costs which takes into account the effects of passenger sell-up, where previous models tend to underestimate the spill cost by only modeling passengers' first choices. The heuristic demonstrates improved estimates of passenger spill: in particular, in restricted fare structures and for moderate amounts of spill, the model exhibits approximate relative errors on the order of 5%, a factor of two improvement over previous models.by Michael Abramovich.S.M