Airline schedule planning and operations : optimization-based approaches for delay mitigation

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 Studie

Robust optimization for network-based resource allocation problems under uncertainty

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, 2007.Includes bibliographical references (p. 129-131).We consider large-scale, network-based, resource allocation problems under uncertainty, with specific focus on the class of problems referred to as multi-commodity flow problems with time-windows. These problems are at the core of many network-based resource allocation problems. Inherent data uncertainty in the problem guarantees that deterministic optimal solutions are rarely, if ever, executed. Our work examines methods of proactive planning, that is, robust plan generation to protect against future uncertainty. By modeling uncertainties in data corresponding to service times, resource availability, supplies and demands, we can generate solutions that are more robust operationally, that is, more likely to be executed or easier to repair when disrupted. The challenges are the following: approaches to achieve robustness 1) can be extremely problem-specific and not general; 2) suffer from issues of tractability; or 3) have unrealistic data requirements. We propose in this work a modeling and algorithmic framework that addresses the above challenges.(cont.) Our modeling framework involves a decomposition scheme that separates problems involving multi-commodity flows with time-windows into routing (that is, a routing master problem) and scheduling modules (that is, a scheduling sub-problem), and uses an iterative scheme to provide feedback between the two modules, both of which are more tractable than the integrated model. The master problem has the structure of a multi-commodity flow problem and the sub-problem is a set of network flow problems. This decomposition allows us to capture uncertainty while maintaining tractability. Uncertainty is captured in part by the master problem and in part by the sub-problem. In addition to solving problems under uncertainty, our decomposition scheme can also be used to solve large-scale resource allocation problems without uncertainty. As proof-of-concept, we apply our approach to a vehicle routing and scheduling problem and compare its solutions to those of other robust optimization approaches. Finally, we propose a framework to extend our robust, decomposition approach to the more complex problem of network design.by Lavanya Marla.S.M

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

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

A decomposition approach for commodity pickup and delivery with time-windows under uncertainty

We consider a special class of large-scale, network-based, resource allocation problems under uncertainty, namely that of multi-commodity flows with time-windows under uncertainty. In this class, we focus on problems involving commodity pickup and delivery with time-windows. Our work examines methods of proactive planning, that is, robust plan generation to protect against future uncertainty. By a priori modeling uncertainties in data corresponding to service times, resource availability, supplies and demands, we generate solutions that are more robust operationally, that is, more likely to be executed or easier to repair when disrupted. We propose a novel modeling and solution framework involving a decomposition scheme that separates problems into a routing master problem and Scheduling Sub-Problems; and iterates to find the optimal solution. Uncertainty is captured in part by the master problem and in part by the Scheduling Sub-Problem. We present proof-of-concept for our approach using real data involving routing and scheduling for a large shipment carrier’s ground network, and demonstrate the improved robustness of solutions from our approach

Integrated Disruption Management and Flight Planning to Trade Off Delays and Fuel Burn

In this paper we present a novel approach addressing airline delays and recovery. Airline schedule recovery involves making decisions during operations to minimize additional operating costs while getting back on schedule as quickly as possible. The mechanisms used include aircraft swaps, flight cancellations, crew swaps, reserve crews, and passenger rebookings. In this context, we introduce another mechanism, namely flight planning that enables flight speed changes. Flight planning is the process of determining flight plan(s) specifying the route of a flight, its speed, and its associated fuel burn. Our key idea in integrating flight planning and disruption management is to adjust the speeds of flights during operations, trading off flying time (and along with it, block time) and fuel burn; in combination with existing mechanisms, such as flight holds. Our goal is striking the right balance of fuel costs and passenger-related delay costs incurred by the airline.We present both exact and approximate models for integrated aircraft and passenger recovery with flight planning. From computational experiments on data provided by a European airline, we estimate that the ability of our approach to control (decrease or increase) flying time by trading off with fuel burn, as well as to hold downstream flights, results in reductions in passenger disruptions by approximately 66%-83%, accompanied by small increases in fuel burn of 0.152%-0.155% and a total cost savings of approximately 5.7%-5.9% for the airline, may be achieved compared to baseline approaches typically used in practice. We discuss the relative benefits of two mechanisms studied-specifically, flight speed changes and intentionally holding flight departures, and show significant synergies in applying these mechanisms. The results, compared with recovery without integrated flight planning, are because of increased swap possibilities during recovery, decreased numbers of flight cancellations, and fewer disruptions to passengers

Congestion in a competitive world: a study of the impact of competition on airline operations

Air transport is a fast developing area. Airlines compete for a limited resource, namely airport capacity. The consequence is an increase in airport congestion, which generates huge delays that are enhanced due to delay propagation through the whole network. Currently, in the US, the Federal Aviation Association (FAA) only controls operational capacity allocation when disruptions occur with Ground Delay Programs (GDPs), and airlines are free to schedule their operations. In this paper, we propose a theoretical framework allowing to evaluate different regulations or incentives