Aircraft Maintenance Routing Problem – A Literature Survey

The airline industry has shown significant growth in the last decade according to some indicators such as annual average growth in global air traffic passenger demand and growth rate in the global air transport fleet. This inevitable progress makes the airline industry challenging and forces airline companies to produce a range of solutions that increase consumer loyalty to the brand. These solutions to reduce the high costs encountered in airline operations, prevent delays in planned departure times, improve service quality, or reduce environmental impacts can be diversified according to the need. Although one can refer to past surveys, it is not sufficient to cover the rich literature of airline scheduling, especially for the last decade. This study aims to fill this gap by reviewing the airline operations related papers published between 2009 and 2019, and focus on the ones especially in the aircraft maintenance routing area which seems a promising branch

Attractiveness-based airline network models with embedded spill and recapture

Purpose: In airline revenue management, the modeling of the spill and recapture effects is essential for an accurate estimation of the passenger flow and the revenue in a flight network. However, as most current approaches toward spill and recapture involve either non-linearity or a tremendous amount of additional variables, it is computationally intractable to apply those techniques to the classical network design and capacity planning models. Design/methodology: We present a new framework that incorporates the spill and recapture effects, where the spill from an itinerary is recaptured by other itineraries based on their attractiveness. The presented framework distributes the accepted demand of an itinerary according to the currently available itineraries, without adding extra variables for the recaptured spill. Due to its compactness, we integrate the framework with the classical capacity planning and network design models. Findings: Our preliminary computational study shows an increase of 1.07% in profitability anda better utilization of the network capacity, on a medium-size North American airline provided by Sabre Airline Solutions. Originality/value: Our investigation leads to a holistic model that tackles the network design and capacity planning simultaneously with an accurate modeling of the spill and re- capture effects.Furthermore, the presented framework for spill and recapture is versatile and can be easily applied to other disciplines such as the hospitality industry and product line design (PLD) problems.Peer Reviewe

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

An integrated mathematical model of crew scheduling

In conditions of air transport companies, the process of planning flight schedules is one of the most important processes each airline has to deal with. The flight schedule planning process consists of several consecutive plans. The first step of the planning process is defining which air routes will be operated, the decision is based on the business plan of the air transport company. Consequently, suitable airplanes have to be assigned to the individual air routes. And finally, on the basis of the pre-vious steps shifts of pilots can be planned, the shifts are usually planned one month in advance. However, with no respect to the created plan some unexpected disruptions of the flying staff, especially of the pilots, may happen in practice due to many reasons. In such cases the original plan has to be modified in order to react to the disruptions. The modifications can represent an optimisation problem – the air transport company has a set of the pilots and on the basis of their qualification and experience the company has to create new aircrews. The pilots can be found in different localities that are different from the airports of the planned flight departures. That means the newly planned aircrews are assigned to the individual flights with respect to costs associated with transportation of the aircrews to the airports of their departure. The problem can be solved by many approaches. One of the possible approaches is a heuristic approach which is based on sequential solving two linear mathematical models. The first model decides about the aircrews (matches the pilots with respect to their compatibility). The second model solves the assignment problem – the air-crews are matched with the individual flights. The article presents an integrated linear model which deals with both problems at the same time.V podmínkách leteckých dopravců je hlavním výsledkem plánovacího procesu letový řád. Samotná tvorba letového řádu je posloupností několika na sebe navazujících dílčích plánů. Prvním krokem v procesu plánování je naplánování linek podle obchodního záměru dopravce, následně se naplánovaným letům přidělí konkrétní typ letadla. Zpravidla s měsíčním předstihem je nutné vytvořit plán práce pro posádky pilotů, kteří budou letouny obsluhovat. Bez ohledu na vytvořený plán práce posádek však může dojít k neočekávaným výpadkům personálu. Potom je nutné operativně upravit připravený plán a posádky přeplánovat. Jedná se tedy o optimalizační problém, kdy dopravce má k dispozici množinu pilotů, z nichž je nutné na základě jejich kvalifikace a zkušeností vytvořit nové posádky. Piloti se mohou nacházet v různých destinacích, které mohou být různé od letišť odletů. Nově vytvořené posádky jsou potom přidělovány konkrétním letadlům v závislosti na velikosti nákladů spojených s přepravou posádek k letadlům. Uvedený problém lze řešit různými způsoby. První způsob je heuristický založený na postupném řešení dvou lineárních modelů. V prvním modelu se rozhoduje o vytvoření posádek. Druhý model vytvořené posádky přiděluje letadlům. Cílem tohoto příspěvku bude prezentovat integrovaný lineární model řešící oba problémy současně

New approaches to airline recovery problems

Air traffic disruptions result in fight delays, cancellations, passenger misconnections, creating high costs to aviation stakeholders. This dissertation studies two directions in the area of airline disruption management – an area of significant focus in reducing airlines’ operating costs. These directions are: (i) a joint proactive and reactive approach to airline disruption management, and (ii) a dynamic aircraft and passenger recovery approach to evaluate the long-term effects of climate change on airline network recoverability. Our first direction proposes a joint proactive and reactive approach to airline disruption management, which optimizes recovery decisions in response to realized disruptions and in anticipation of future disruptions. Specifically, it forecasts future disruptions partially and probabilistically by estimating systemic delays at hub airports (and the uncertainty thereof) and ignoring other contingent disruption sources. It formulates a dynamic stochastic integer programming framework to minimize network-wide expected disruption recovery costs. Specifically, our Stochastic Reactive and Proactive Disruption Management (SRPDM) model combines a stochastic queuing model of airport congestion, a fight planning tool from Boeing/Jeppesen and an integer programming model of airline disruption recovery. We develop an online solution procedure based on look-ahead approximation and sample average approximation, which enables the model's implementation in short computational times. Experimental results show that leveraging partial and probabilistic estimates of future disruptions can reduce expected recovery costs by 1-2%, as compared to a baseline myopic approach that uses realized disruptions alone. These benefits are mainly driven by the deliberate introduction of departure holds to reduce expected fuel costs, fight cancellations and aircraft swaps. Our next direction studies the impact of climate change-imposed constraints on the recoverability of airline networks. We first use models that capture the modified payload-range curves for different aircraft types under multiple climate change scenarios, and the associated (reduced) aircraft capacities. We next construct a modeling and algorithmic framework that allows for simultaneous and integrated aircraft and passenger recovery that explicitly capture the above-mentioned capacity changes in aircraft at different times of day. Our computational results using the climate model on a worst-case, medium-case, and mild-case climate change scenarios project that daily total airline recovery costs increase on average, by 25% to 55.9% on average ; and by 10.6% to 156% over individual disrupted days. Aircraft-related costs are driven by a huge increase in aircraft swaps and cancelations; and passenger-related costs are driven by increases in disrupted passengers who need to be rebooked on the same or a different airline. Our work motivates the critical need for airlines to systematically incorporate climate change as a factor in the design of aircraft as well as in the design and operations of airline networks

A Classification and Assessment of Research Streams on Low Cost Modeling in Civil Aviation Transportation Industry

This article attempts to identify key research streams in Civil Aviation Transportation Industry during the past decade and highlights the evolution of the literature. Progress in six established research thrusts and a new research stream is discussed. Using content analysis, the existing research is also examined from a methodological point of view. The review provides evidence for an increasingly sophisticated and rich body of knowledge in global Civil Aviation Transportation Industry. Keywords: Civil Aviation Transportation Industry (CATI), Low Cost Strategies (LCS), Low Cost Carriers (LCCs), Classification, Assessment

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