Integrating the fleet assignment model with uncertain demand

This thesis was submitted for the degree of Master of Philosophy and awarded by Brunel University.One of the main challenges facing the airline industry is planning under uncertainty, especially in the context of schedule disruptions. The robust models and solution algorithms that have been proposed and developed to handle the uncertain parameters will be discussed. Fleet assignment models (FAM) are used by many airlines to assign aircraft to fights in a schedule to maximize profit. In the context of FAM, the goal of robustness is to produce solutions that perform well relative to uncertainties in demand and operation. In this thesis, we introduce new FAMs (i.e. DFAM1 and DFAM2) that tackles the common problem associated with aircraft utilization. Subsequently, stochastic programming (SP) is presented as a method of choice for the research. Through the use of a two-stage SP with recourse technique, the DFAMs are extended to SP-FAMs (SP-FAM1 and SP-FAM2). The main distinction of the SP-FAM compared with other FAMs is that, given a stochastic passenger demand, it gives a strategic fleet assignment solution that hedges against all possible tactical solutions. In addition, we have a tactical solution for every scenario. In generating the demand scenarios, we use a network-simulation model embedded with a time-series engine that gives a snapshot of one week that is representative of any other week of the scheduling season. We later outline the approach of solving the SP-FAMs where the schedule is compacted through several preprocessing steps before inputting it into SAS-AMPL converter. The SAS-AMPL converter prepares all the data into readable AMPL format. Finally, we execute the optimizer using a FortMP solver (integrated in AMPL) that invokes branch-and-bound algorithm. We give a proof of concept using real data from a Middle East airline. Our investigations establish clear benefits of the recourse FAM compared to alternative models. Finally, we propose areas of future research to improve SP-FAM robustness through solution algorithms, revenue management (RM) effects, calibration of network-simulation models and system integration

Robust airline schedule planning : review and development of optimization approaches

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

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

Hybrid methods for integrated aircraft routing and crew pairing problem with short flight legs

The aircraft routing and crew pairing problems are two processes that are difficult to be solved in the airline operations planning due to the rules that each flight leg needs to be operated on by one aircraft and one crew pair. These two problems, though interrelated in practice, are usually solved sequentially and often leads to suboptimal solution. Thus, this research contributes to the solution of the integrated aircraft routing and crew pairing problem in order to determine the minimum cost of this integrated problem where each flight leg is covered by one aircraft and one crew pair. This study also considers short connection between two flight legs in order to ensure that the crews do not change the aircraft if the connection time is in between 20 to 59 minutes. Another consideration is the restricted connection that imposes penalty costs when the second flight leg uses the same crew but not the same aircraft. Based on the literature review, most of the existing solutions concentrate on minimizing the planned costs. Although the minimum costs are significantly important in airline operations planning, the efficiency of a solution method in terms of computational time cannot be neglected. It is necessary to solve the integrated problem by using an efficient model that is able to generate a good high quality solution in a short time as requested by the airline industry. In order to solve the problem, a set of feasible aircraft routes and crew pairs are initially generated to be used as the input data in solving the integrated model effectively. There are two heuristic methods which are proposed in generating the set of feasible aircraft routes and crew pairs namely constructive-based heuristic and Genetic Algorithm (GA). The generated feasible aircraft routes and crew pairs are then used in solving the integrated problem by using Integer Linear Programming (ILP) method, Dantzig Wolfe Decomposition method, Benders Decomposition method and Particle Swarm. Computational results obtained from these methods are then compared by testing them on four types of aircraft with different number of flight legs based on Malaysia local flights for one week flight cycle. From the numerical results, it can be concluded that the proposed methods are more efficient compared to the ILP method available in the literature in terms of the computational time where the hybrid algorithm of GA and Benders Decomposition is found to be advantageous compared to the others. The maximum cost deviation of only 4.77% also justifies the strength of this hybrid algorithm. One possible future research that can be extended from this study would be the development of an algorithm that incorporates a parallel GA within the proposed methods for larger instances which are likely to exist in international flights in order to speed up the planning process

Solving a Large-Scale Integrated Fleet Assignment and Crew Pairing Problem

Airline schedule planning problems are typically decomposed into smaller problems, which are solved in a sequential manner, due to the complexity of the overall problems. This results in suboptimal solutions as well as feasibility issues in the consecutive phases. In this study, we address the integrated fleet assignment and crew pairing problem (IFACPP) of a European Airline. The specific network and cost structures allow us to develop novel approaches to this integrated problem. We propose an optimization-driven algorithm that can efficiently handle large scale instances of the IFACPP. We perform a computational study on real-world monthly flight schedules to test the performance of our solution method. Based on the results on instances with up to 27,500 flight legs, we show that our algorithm provides solutions with significant cost savings over the sequential approach.Scopu

Integrated aircraft scheduling problem: An auto-adapting algorithm to find robust aircraft assignments for large flight plans

The overall airline scheduling process involves hierarchical steps starting with the network design and ending with crew assignment. Aircraft routing is especially important with respect to timing and costs for an airline. In this contribution, we focus on aircraft routing where aircraft are assigned to flight legs further considering maintenance requirements. We developed and implemented algorithms that extend the aircraft routing problem (ARP) by including profit and robustness. The latter objective is important as the dependencies of flights and airlines increases and deviations to the original time plan as unexpected events like volcano eruptions or heavy weather-related issues are difficult to handle. A robust aircraft routing ensures that unforeseen events have less impact. The results are compared to current state-of-the-art solutions. We developed a test instance-generator to create specific problems and build a library for future benchmarking tests

Integrated fleet assignment with cargo routing

Master'sMASTER OF ENGINEERIN