A Rolling Horizon Based Algorithm for Solving Integrated Airline Schedule Recovery Problem

Airline disruption incurred huge cost for airlines and serious inconvenience for travelers. In this paper, we study the integrated airline schedule recovery problem, which considers flight recovery, aircraft recovery and crew recovery simultaneously. First we built an integer programming model which is based on traditional set partitioning model but including flight copy decision variables. Then a rolling horizon based algorithm is proposed to efficiently solve the model. Our algorithm decomposes the whole problem into smaller sub-problems by restricting swapping opportunities within each rolling period. All the flights are considered in each sub-problem to circumvent ‘myopic’ of traditional rolling horizon algorithm. Experimental results show that our method can provide competitive recovery solution in both solution quality and computation time.published_or_final_versio

An Irregular Flight Scheduling Model and Algorithm under the Uncertainty Theory

The flight scheduling is a real-time optimization problem. Whenever the schedule is disrupted, it will not only cause inconvenience to passenger, but also bring about a large amount of operational losses to airlines. Especially in case an irregular flight happens, the event is unanticipated frequently. In order to obtain an optimal policy in airline operations, this paper presents a model in which the total delay minutes of passengers are considered as the optimization objective through reassigning fleets in response to the irregular flights and which takes into account available resources and the estimated cost of airlines. Owing to the uncertainty of the problem and insufficient data in the decision-making procedure, the traditional modeling tool (probability theory) is abandoned, the uncertainty theory is applied to address the issues, and an uncertain programming model is developed with the chance constraint. This paper also constructs a solution method to solve the model based on the classical Hungarian algorithm under uncertain conditions. Numerical example illustrates that the model and its algorithm are feasible to deal with the issue of irregular flight recovery

Railway Crew Rescheduling with Retiming

Railway operations are disrupted frequently, e.g. the Dutch railway network experiences about three large disruptions per day on average. In such a disrupted situation railway operators need to quickly adjust their resource schedules. Nowadays, the timetable, the rolling stock and the crew schedule are recovered in a sequential way. In this paper, we model and solve the crew rescheduling problem with retiming. This problem extends the crew rescheduling problem by the possibility to delay the departure of some trains. In this way we partly integrate timetable adjustment and crew rescheduling. The algorithm is based on column generation techniques combined with Lagrangian heuristics. In order to prevent a large increase in computational time, retiming is allowed only for a limited number of trains where it seems very promising. Computational experiments with real-life disruption data show that, compared to the classical approach, it is possible to find better solutions by using crew rescheduling with retiming.

Minimizing airport peaks problem by improving airline operations performance through an agent based system

Airports are important infra-structures for the air transportationbusiness. One of the major operational constraints is the peak of passengers inspecific periods of time. Airline companies take into consideration the airportcapacity when building the airline schedule and, because of that, the executionof the airline operational plan can contribute to improve or avoid airport peakproblems. The Airline Operations Control Center (AOCC) tries to solveunexpected problems that might occur during the airline operation. Problemsrelated to aircrafts, crewmembers and passengers are common and the actionstowards the solution of these problems are usually known as operationsrecovery. In this paper we propose a way of measuring the AOCC performancethat takes into consideration the relation that exists between airline scheduleand airport peaks. The implementation of a Distributed Multi-Agent System(MAS) representing the existing roles in an AOCC, is presented. We show thatthe MAS contributes to minimize airport peaks without increasing theoperational costs of the airlines

Large-scale mixed integer optimization approaches for scheduling airline operations under irregularity

Perhaps no single industry has benefited more from advancements in computation, analytics, and optimization than the airline industry. Operations Research (OR) is now ubiquitous in the way airlines develop their schedules, price their itineraries, manage their fleet, route their aircraft, and schedule their crew. These problems, among others, are well-known to industry practitioners and academics alike and arise within the context of the planning environment which takes place well in advance of the date of departure. One salient feature of the planning environment is that decisions are made in a frictionless environment that do not consider perturbations to an existing schedule. Airline operations are rife with disruptions caused by factors such as convective weather, aircraft failure, air traffic control restrictions, network effects, among other irregularities. Substantially less work in the OR community has been examined within the context of the real-time operational environment. While problems in the planning and operational environments are similar from a mathematical perspective, the complexity of the operational environment is exacerbated by two factors. First, decisions need to be made in as close to real-time as possible. Unlike the planning phase, decision-makers do not have hours of time to return a decision. Secondly, there are a host of operational considerations in which complex rules mandated by regulatory agencies like the Federal Administration Association (FAA), airline requirements, or union rules. Such restrictions often make finding even a feasible set of re-scheduling decisions an arduous task, let alone the global optimum. The goals and objectives of this thesis are found in Chapter 1. Chapter 2 provides an overview airline operations and the current practices of disruption management employed at most airlines. Both the causes and the costs associated with irregular operations are surveyed. The role of airline Operations Control Center (OCC) is discussed in which serves as the real-time decision making environment that is important to understand for the body of this work. Chapter 3 introduces an optimization-based approach to solve the Airline Integrated Recovery (AIR) problem that simultaneously solves re-scheduling decisions for the operating schedule, aircraft routings, crew assignments, and passenger itineraries. The methodology is validated by using real-world industrial data from a U.S. hub-and-spoke regional carrier and we show how the incumbent approach can dominate the incumbent sequential approach in way that is amenable to the operational constraints imposed by a decision-making environment. Computational effort is central to the efficacy of any algorithm present in a real-time decision making environment such as an OCC. The latter two chapters illustrate various methods that are shown to expedite more traditional large-scale optimization methods that are applicable a wide family of optimization problems, including the AIR problem. Chapter 4 shows how delayed constraint generation and column generation may be used simultaneously through use of alternate polyhedra that verify whether or not a given cut that has been generated from a subset of variables remains globally valid. While Benders' decomposition is a well-known algorithm to solve problems exhibiting a block structure, one possible drawback is slow convergence. Expediting Benders' decomposition has been explored in the literature through model reformulation, improving bounds, and cut selection strategies, but little has been studied how to strengthen a standard cut. Chapter 5 examines four methods for the convergence may be accelerated through an affine transformation into the interior of the feasible set, generating a split cut induced by a standard Benders' inequality, sequential lifting, and superadditive lifting over a relaxation of a multi-row system. It is shown that the first two methods yield the most promising results within the context of an AIR model.PhDCommittee Co-Chair: Clarke, John-Paul; Committee Co-Chair: Johnson, Ellis; Committee Member: Ahmed, Shabbir; Committee Member: Clarke, Michael; Committee Member: Nemhauser, Georg

FLIGHT RISK MANAGEMENT AND CREW RESERVE OPTIMIZATION

There are two key concerns in the development process of aviation. One is safety, and the other is cost. An airline running with high safety and low cost must be the most competitive one in the market. This work investigates two research efforts respectively relevant to these two concerns. When building support of a real time Flight Risk Assessment and Mitigation System (FRAMS), a sequential multi-stage approach is developed. The whole risk management process is considered in order to improve the safety of each flight by integrating AHP and FTA technique to describe the framework of all levels of risks through risk score. Unlike traditional fault tree analysis, severity level, time level and synergy effect are taken into account when calculating the risk score for each flight. A risk tree is designed for risk data with flat shape structure and a time sensitive optimization model is developed to support decision making of how to mitigate risk with as little cost as possible. A case study is solved in reasonable time to approve that the model is practical for the real time system. On the other hand, an intense competitive environment makes cost controlling more and more important for airlines. An integrated approach is developed for improving the efficiency of reserve crew scheduling which can contribute to decrease cost. Unlike the other technique, this approach integrates the demand forecasting, reserve pattern generation and optimization. A reserve forecasting tool is developed based on a large data base. The expected value of each type of dropped trip is the output of this tool based on the predicted dropping rate and the total scheduled trips. The rounding step in current applied methods is avoided to keep as much information as possible. The forecasting stage is extended to the optimization stage through the input of these expected values. A novel optimization model with column generation algorithm is developed to generate patterns to cover these expected level reserve demands with minimization to the total cost. The many-to-many covering mode makes the model avoid the influence of forecasting errors caused by high uncertainty as much as possible