272 research outputs found
Network Structures of Cargo Airlines - An Empirical and a Modelling Approach
The development of efficient air freight networks is an upcoming challenge. The present book approaches this problem for cargo airlines by characterising and classifying their network structures and by developing a model for an airline\u27s strategic network design. The book provides results which are of value for airline professionals (network efficiency analysis), policy makers (policy impact assessment) and researchers (cargo airline network design model)
Scheduling airline reserve crew using a probabilistic crew absence and recovery model
Airlines require reserve crew to replace delayed or absent crew, with the aim of preventing consequent flight cancellations. A reserve crew schedule specifies the duty periods for which different reserve crew will be on standby to replace any absent crew. For both legal and health-and-safety reasons the reserve crew's duty period is limited, so it is vital that these reserve crew are available at the right times, when they are most likely to be needed and will be most effective. Scheduling a reserve crew unnecessarily, or earlier than needed, wastes reserve crew capacity. Scheduling a reserve crew too late means either an unrecoverable cancellation or a delay waiting for the reserve crew to be available. Determining when to schedule these crew can be a complex problem , since one crew member could potentially cover a vacancy on any one of a number of different flights, and flights interact with each other, so a delay or cancellation for one flight can affect a number of later flights. This work develops an enhanced mathematical model for assessing the impact of any given reserve crew schedule, in terms of reduced total expected cancellations and any resultant reserve induced delays, whilst taking all of the available information into account, including the schedule structure and interactions between flights, the uncertainties involved, and the potential for multiple crew absences on a single flight. The interactions between flights have traditionally made it very hard to predict the effects of cancellations or delays, and hence to predict when best to allocate reserve crew and lengthy simulation runs have traditionally been used to make these predictions. This work is motivated by the airline industry's need for improved mathematical models to replace the time-consuming simulation-based approaches. The improved predictive probabilistic model which is introduced here is shown to produce results that match a simulation model to a high degree of accuracy, in a much shorter time, making it an effective and accurate surrogate for simulation. The modelling of the problem also provides insights into the complexity of the problem that a purely simulation based approach would miss. The increased speed enables potential deployment within a real time decision support context, comparing alternative recovery decisions as disruptions occur. To illustrate this, the model is used in this paper as a fitness function in meta-heuristics algorithms to generate disruption minimising reserve crew schedules for a real airline schedule. These are shown to be of a high quality, demonstrating the effectiveness and reliability of the proposed approach
Aircraft Maintenance Routing Problem ā 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
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%
Airline reserve crew scheduling under uncertainty
This thesis addresses the problem of airline reserve crew scheduling under crew absence and journey time uncertainty. This work is primarily concerned with the allocation of reserve crew to standby duty periods. The times at which reserve crew are on duty, determine which possible crew absence or delay disruptions they can be used to absorb. When scheduling reserve crew, the goal is to minimise the expected levels of delay and cancellation disruptions that occur on the day of operation. This work introduces detailed probabilistic models of the occurrence of crew absence and delay disruptions and how reserve crew are used to absorb such disruptions. Firstly, separate probabilistic models are developed for crew absence and delay disruptions. Then, an integrated probabilistic model of absence and delay disruptions is introduced, which accounts for: delays from all causes; delay propagation; cancellations resulting from excessive delays and crew absence; the use of reserve crew to cover such disruptions given a reserve policy; and the possibility of swap recovery actions as an alternative delay recovery action. The model yields delay and cancellation predictions that match those derived from simulation to a high level of accuracy and does so in a fraction of the time required by simulation. The various probabilistic models are used in various search methodologies to find disruption minimising reserve crew schedules. The results show that high quality reserve crew schedules can be derived using a probabilistic model.
A scenario-based mixed integer programming approach to modelling operational uncertainty and reserve crew use is also developed in this thesis and applied to the problem of reserve crew scheduling. A scenario selection heuristic is introduced which improves reserve crew schedule quality using fewer input scenarios.
The secondary objective of this thesis is to investigate the effect of the reserve policy used on the day of operation, that is, determining when and which reserve crew should be utilised. The questions of how reserve policies can be improved and how they should be taken into account when scheduling reserve crew are addressed. It was found that the approaches developed for reserve crew scheduling lend themselves well to an online application, that is, using them to evaluate alternative reserve decisions to ensure reserve crew are used as effectively as possible. In general it is shown that `day of operation' disruptions can be significantly reduced through both improved reserve crew schedules and/or reserve policies. This thesis also points the way towards future research based on the proposed approaches
DISRUPTION RECOVERY IN COMMERCIAL AVIATION
This thesis presents three major contributions for commercial aviation planning and disruption recovery in commercial aviation. The first contribution presented in this thesis consists of a flight planning model to calculate Block Time and Fuel (BTF) consumed for an aircraft model during the flight. The BTF model computes the ground distance between the origin and destination airports, derives the flightās cruise altitude, and by integrating two institutional data sets calculates the duration and the fuel consumed for the whole of taxi-out, take-off, climb, cruise, descent, approach, landing, and taxi-in phases. The model renders very good results for block time and consumed fuel however, it does not consider aircraft weight loss neither the influence of the wind. The second contribution of this thesis consists of a recovery procedure for disrupted aircraft rotations, the Constructive Heuristic for the Aircraft Recovery Problem (CHARP). The CHARP recovers the infeasible rotation combining a meta-heuristic that performs a pincer movement over the search space and Constraint Programming (CP). Additionally, the CHARP uses Constraint Propagation to reduce the size of the search therefore reducing computing. The initial experiments demonstrated that if Constraint Propagation was not used computing time would double. The recovery strategy included flight creation delays and cancellations however it did not include aircraft swap. The third contribution of this thesis combines the BTF model and the CHARP. Since the BTF model returns lower block time flights than those used by the CHARP this thesis investigates six disruption scenarios with shorter block time
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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
Upravljanje putanjama vazduhoplova u kontroli letenja na pre-taktiÄkom i taktiÄkom nivou
Global air traffic demand is continuously increasing, and it is predicted
to be tripled by 2050. The need for increasing air traffic capacity motivates a
shift of ATM towards Trajectory Based Operations (TBOs). This implies the
possibility to design efficient congestion-free aircraft trajectories more in
advance (pre-tactical, strategic level) reducing controllerās workload on tactical
level. As consequence, controllers will be able to manage more flights.
Current flow management practices in air traffic management (ATM)
system shows that under the present system settings there are only timid
demand management actions taken prior to the day of operation such as: slot
allocation and strategic flow rerouting. But the choice of air route for a
particular flight is seen as a commercial decision to be taken by airlines, given
air traffic control constraints. This thesis investigates the potential of robust
trajectory planning (considered as an additional demand management action)
at pre-tactical level as a mean to alleviate the en-route congestion in airspace.
Robust trajectory planning (RTP) involves generation of congestion-free
trajectories with minimum operating cost taking into account uncertainty of
trajectory prediction and unforeseen event. Although planned cost could be
higher than of conventional models, adding robustness to schedules might
reduce cost of disruptions and hopefully lead to reductions in operating cost.
The most of existing trajectory planning models consider finding of conflict-free
trajectories without taking into account uncertainty of trajectory prediction. It is
shown in the thesis that in the case of traffic disturbances, it is better to have a
robust solution otherwise newly generated congestion problems would be hard
and costly to solve.
This thesis introduces a novel approach for route generation (3D
trajectory) based on homotopic feature of continuous functions. It is shown that
this approach is capable of generating a large number of route shapes with a
reasonable number of decision variables. Those shapes are then coupled with
time dimension in order to create trajectories (4D)...Globalna potražnja za vazduÅ”nim saobraÄajem u stalnom je porastu i
prognozira se da Äe broj letova biti utrostruÄen do 2050 godine. Potreba za
poveÄanjem kapaciteta sistema vazduÅ”nog saobraÄaja motivisala je promene u
sistemu upravljanja saobraÄajnim tokovima u kome Äe u buduÄnosti centralnu
ulogu imati putanje vazduhoplova tzv. ātrajectory-basedā koncept. Takav
sistem omoguÄiÄe planiranje putanja vazduhoplova koje ne stvaraju zaguÅ”enja
u sistemu na pre-taktiÄkom nivou i time smanjiti radno optereÄenje kontrolora
na taktiÄkom nivou. Kao posledica, kontrolor Äe moÄi da upravlja viÅ”e letova
nego u danaŔnjem sistemu.
DanaÅ”nja praksa upravljanja saobraÄajnim tokovima pokazuje da se mali
broj upravljaÄkih akcija primenjuje pre dana obavljanja letova npr.: alokacija
slotova poletanja i strateÅ”ko upravljanje saobraÄajnim tokovima. MeÄutim izbor
putanje kojom Äe se odviti let posmatra se kao komercijalna odluka aviokompanije
(uz poÅ”tovanje postavljenih ograniÄenja od strane kontrole letenja) i
stoga je ostavljen na izbor avio-kompaniji. VeÄina, do danas razvijenih, modela
upravljanja putanjama vazduhoplova ima za cilj generisanje bez-konfliktnih
putanja, ne uzimajuÄi u obzir neizvesnost u poziciji vazduhoplova. U ovoj
doktorskoj disertaciji ispitivano je planiranje robustnih putanja vazduhoplova
(RTP) na pre-taktiÄkom nivou kao sredstvo ublažavanja zaguÅ”enja u
vazduŔnom prostoru . Robustno upravljanje putanjama vazduhoplova
podrazumeva izbor putanja vazduhoplova sa minimalnim operativnim
troŔkovima koje ne izazivaju zaguŔenja u vazduŔnom prostoru u uslovima
neizvesnosti buduÄe pozicije vazduhoplova i nepredviÄenih dogaÄaja. Iako
predviÄeni (planirani) operativni troÅ”kovi robustnih putanja mogu u startu biti
veÄi od operativnih troÅ”kova bez-konfliktnih putanja, robusnost može uticati na
smanjenje troÅ”kove poremeÄaja putanja jer ne zahteva dodatnu promenu
putanja vazduhplova radi izbegavanja konfliktnih situacija na taktiÄkom nivou.
To na kraju može dovesti i do smanjenja stvarnih operativnih troŔkova. U tezi je
pokazano, da je u sluÄaju poremeÄaja saobraÄaja bolje imati robustno reÅ”enje
(putanje), jer novo-nastali problem zaguŔenosti vazduŔnog prostora je teŔko i
skupo reŔiti..
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