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![Figure 3. Turnaround Time Schedule (A380, 90min, baseline)[7]](profile/Michael-Schultz-20/publication/262567633/figure/fig3/AS:667715450712064@1536207177021/Turnaround-Time-Schedule-A380-90min-baseline7_Q320.jpg)
During 2007, 19% of all European flights were more than 15 min late. One contributor to this delay is the insufficient ground operation performance inducing excessive process durations. Whenever these processes are part of the critical Turnaround (TA) path, such as de-boarding, fuelling, cleaning, catering and boarding, the effects immediately prop...
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... valid datasets 1 , the 1 It may be noted that few operations were found with a turnaround time up to 2 hrs, which were not considered following distribution with a mean time of μ = 61.1 min (σ = 8.9 min) was found: Further on, this data set was analyzed against the individual delays of each operation to allow correlating turnaround durations and. Fig. 6 shows the results of that analysis, with an average arrival delay of μ ARR = 2.4 min and an average departure delay μ DEP = 8.4 min. It was further found that departures leave the airport "early" for only 1% of all cases. The delay distribution shows 56% being less than 5 min late, 24% more than 5 and less than 15 min late and 19% ...
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Citations
... Scene factors [15][16][17] Airport operation status, flight arrival time period Airport queue waiting efficiency, airport layout Information (passenger, luggage, seat, cargo, mail, etc.) Airport identification recognition Flight factors [15,16] Pre-flight arrival delay time, aircraft type Plan turnaround time, transshipment volume (oil and water) ...
... Scene factors [15][16][17] Airport operation status, flight arrival time period Airport queue waiting efficiency, airport layout Information (passenger, luggage, seat, cargo, mail, etc.) Airport identification recognition Flight factors [15,16] Pre-flight arrival delay time, aircraft type Plan turnaround time, transshipment volume (oil and water) ...
Flight delay identification is an important way to coordinate the operation time of airport ground service providers and improve the efficiency of airport operations. By analyzing the flight turnaround operation process, considering the randomness and synchronization of the turnaround process, and using Colored Petri Nets and Python (4.0.1), we explore the correlation between various links in the flight turnaround process and the take-off delay at the next station. This paper is committed to improving the service performance of airports and airlines, dynamically predicting flight delays, and providing guidance for avoiding excessive time in the actual operation of bad combinations. The results show that there are six kinds of bad combinations in the departure slip-out link, which is the most likely to affect the transit time. The maximum lifting degree in the bad combination is 2.043, and the maximum average delay time in the bad combination is 22.5 min. When the combination of passenger boarding and departure slip-out time is too long, it has a great positive correlation with delay. When the other links are in a state of being able to pass the station on time, the departure time and baggage loading and unloading are the two links that most affect the flight delay value.
... Due to the fact that the true distribution of samples is unknown, we used goodness-of-ft measures with curve ftting methods to empirically ft distributions of sample data. Previous studies on time distributions of subprocesses have generally considered only one distribution [3] or a few distributions [39], ignoring the variability in the duration distribution of diferent activities. Hence, this paper takes several kinds of distributions into account, including Normal, Lognormal, Gamma, Logistic, Exponential, Weibull, and Rayleigh Distribution, being expressed in Appendix A. ...
... Given that the durations of subprocesses are stochastically distributed and depend on diferent trigger parameters [39], the critical path can be diferent for individual turnarounds [43]. Te critical path method (CPM) was used to identify the critical path of ground handling operations of each fight before and after the pandemic based on simulation data from turnaround operations. ...
Efficient aircraft turnaround operations at airports are vital to ensure overall air traffic network performance. After the outbreak of COVID-19, the traditional aircraft ground handling process has changed significantly due to new requirements put forward by the pandemic prevention and control policy. To better understand how COVID-19 has affected ground handling operations, a discrete-event simulation model of turnaround is established to analyze the change in the whole turnaround process before and after the pandemic. The critical path of turnaround operations was used to identify the significantly affected subprocesses to which airports should pay attention. For a case study on the two busiest airports in China, the aircraft turnaround time increased by about 18% after COVID-19. Cabin cleaning, catering, and passenger embarking were the main processes in causing this increase. By evaluating the impact mechanism of COVID-19 on turnaround operations, the study sheds light on strategic, tactical, and operational approaches for relevant authorities.
... The stochastic influences of distinct sub-processes are available in the literature, such as good Weibull fittings for cleaning, de-boarding, catering, and boarding or Gamma fitting for fueling, as seen in [21]. Furthermore, [21] presents stochastic representations, which relate arrival delay to the beginning of particular sub-processes. ...
... The stochastic influences of distinct sub-processes are available in the literature, such as good Weibull fittings for cleaning, de-boarding, catering, and boarding or Gamma fitting for fueling, as seen in [21]. Furthermore, [21] presents stochastic representations, which relate arrival delay to the beginning of particular sub-processes. It has to be stressed that the distributions are derived from the data of mid-to short-range flights, with a flight time of less than 120 min and turnaround duration less than 75 min at the Frankfurt and Munich airports. ...
... The data were procured from the real operational data of an anonymous airline and the report from CODA [25]. The stochastic durations of sub-processes are available in [21]. ...
This paper presents a concept of a fast-time gate-to-gate simulation environment. The implementation is divided into an air traffic part that uses BADA performance parameters and a simulation of ground processes. The main objective of the flow-based hybrid simulation environment is to cover commercial European air traffic, in order to investigate network-related effects when exposed to disturbances. Based on historic traffic scenarios, the hybrid simulation platform enables the investigation of the local and global effects of a variety of disruptions. With respect to current flow-based models, it is intended to gain better insights into the underlying interdependencies by modelling higher levels of detail for selected parts, whilst covering the whole European air traffic network. After a validation and first calibration of the approach, Monte Carlo simulations, based on flight plans, are performed as proof of concept. This aims to illustrate the local effects of network-wide disturbances and is applied by means of stochastic influences of ground processes, gained from real operational data.
... Thereby, the potential for short-term disturbances increases with higher traffic volumes in periods of increased connectivity -so-called hub-banks. Disturbances can occur during almost all turnaround sub-processes, which all have a stochastic duration arising from the influence of human behaviour, resource availability and productivity (Fricke and Schultz, 2009;Wu and Caves, 2004b). Only a few studies acknowledge that the turnaround as a whole has a stochastic duration, whereas in many approaches it is simplified into one single process with a deterministic duration (typically the minimum ground time determined by the aircraft manufacturer). ...
... However, arrival and departure flights are considered as independent, even if they are operated by the same aircraft. This assumption presents a major limitation if one respects all dependencies a delayed arrival flight may have on the turnaround (Fricke and Schultz, 2009) and, thus, also on the departure flight of the same aircraft. ...
... Though, despite the availability of stochastic target time prediction models (Wu and Caves, 2004b;Carr et al., 2005;Fricke and Schultz, 2009;Schlegel, 2010), turnaround times are still assumed to be static within the APOC environment. Consequently, system-inherent uncertainties relating to transfer and resource dependencies are largely neglected at the pre-tactical planning level. ...
Air Traffic Flow Management (ATFM) and airlines use different paradigms for the prioritisation of flights. While ATFM regards each flight as individual entity when it controls sector capacity utilization, airlines evaluate each flight as part of an aircraft rotation, crew pairing and passenger itinerary. As a result, ATFM slot regulations during capacity constraints are poorly coordinated with the resource interdependencies within an airline network, such that the aircraft turnaround -- as the connecting element or breaking point between individual flights in an airline schedule -- is the major contributor to primary and reactionary delays in Europe. This dissertation bridges the gap between both paradigms by developing an integrated schedule recovery model that enables airlines to define their optimal flight priorities for schedule disturbances arising from ATFM capacity constraints. These priorities consider constrained airport resources and different methods are studied how to communicate them confidentially to external stakeholders for the usage in collaborative solutions, such as the assignment of reserve resources or ATFM slot swapping. The integrated schedule recovery model is an extension of the Resource-Constrained Project Scheduling Problem and integrates aircraft turnaround operations with existing approaches for aircraft, crew and passenger recovery. The model is supposed to provide tactical decision support for airline operations controllers at look-ahead times of more than two hours prior to a scheduled hub bank. System-inherent uncertainties about process deviations and potential future disruptions are incorporated into the optimization via stochastic turnaround process times and the novel concept of stochastic delay cost functions. These functions estimate the costs of delay propagation and derive flight-specific downstream recovery capacities from historical operations data, such that scarce resources at the hub airport can be allocated to the most critical turnarounds. The model is applied to the case study of a network carrier that aims at minimizing its tactical costs from several disturbance scenarios. The case study analysis reveals that optimal recovery solutions are very sensitive to the type, scope and intensity of a disturbance, such that there is neither a general optimal solution for different types of disturbance nor for disturbances of the same kind. Thus, airlines require a flexible and efficient optimization method, which considers the complex interdependencies among their constrained resources and generates context-specific solutions. To determine the efficiency of such an optimization method, its achieved network resilience should be studied in comparison to current procedures over longer periods of operation. For the sample of analysed scenarios in this dissertation, it can be concluded that stand reallocation, ramp direct services, quick-turnaround procedures and flight retiming are very efficient recovery options when only a few flights obtain low and medium delays, i.e., 95% of the season. For disturbances which induce high delay into the entire airline network, a full integration of all considered recovery options is required to achieve a substantial reduction of tactical costs. Thereby, especially arrival and departure slot swapping are valuable options for the airline to redistribute its assigned ATFM delays onto those aircraft that have the least critical constraints in their downstream rotations. The consideration of uncertainties in the downstream airline network reveals that an optimization based on deterministic delay costs may overestimate the tactical costs for the airline. Optimal recovery solutions based on stochastic delay costs differ significantly from the deterministic approach and are observed to result in less passenger rebooking at the hub airport. Furthermore, the proposed schedule recovery model is able to define flight priorities and internal slot values for the airline. Results show that the priorities can be communicated confidentially to ATFM by using the concept of 'Flight Delay Margins', while slot values may support future inter-airline slot trading mechanisms.
... This has been attributed to the increase in carry-on luggage. Further, when an aircraft spends more time in an aircraft bay, it restricts another airline's opportunity to serve the same airport and that is a loss to an airport (Silverio et al. 2013;Fricke and Schultz, 2009). Due to this reason alone the airport service providers and airlines collectively concern about reducing the time taken to complete the turnaround process and to improve the process reliability. ...
... More importantly, it has a higher variance in the turnaround process (Schultz and Fricke, 2016). The boarding process amounting to approximately 1/3 of the total turnaround time (Fricke and Schultz, 2009). Moreover, the studies show that the boarding process is completely driven under the control of passengers (Schultz, 2018). ...
The aircraft boarding process is one of the main activities in the aircraft turnaround process. Improving the reliability of the boarding process has been a major concern of airlines as well as airport service providers. This paper focuses on the activities inside the boarding process and the factors that affect the boarding time and its variability. A Discrete Event Simulation Model is used to analyze these identified factors such as the boarding strategy, the number of luggage, arrival rate, and walking speed of passengers in different stages of the boarding process. The model is then used to compare the impact on average boarding time and its variability by changing these model parameters. Recommendations are given in the conclusion as to what methods would lead to minimizing average time and variability under different circumstances. Further, the new procedures incorporated in the boarding process under COVID-19 regulations are also evaluated.
... The turnaround of each aircraft a ∈ AC is defined by scheduled start and finishing times SIBT e and SOBT f , which are adopted from the schedule. It consists of a network of sub-processes P , in which each sub-process i ∈ P is characterised by a related aircraft (RA i = a) and a related flight (RF i = e, f ), has a variable starting time s i and a duration D i that corresponds to the median of a statistically-fitted time distribution [45]. Links between turnaround sub-processes are determined in the precedence matrix PM ij ⊆ P × P . ...
We provide a mathematical formulation of flight-specific delay cost functions that enables a detailed tactical consideration of how a given flight delay will interact with all downstream constraints in the respective aircraft rotation. These functions are reformulated into stochastic delay cost functions to respect conditional probabilities and increasing uncertainty related to more distant operational constraints. Conditional probabilities are learned from historical operations data, such that typical delay propagation patterns can support the flight prioritization process as a part of tactical airline schedule recovery. A case study compares the impact of deterministic and stochastic cost functions on optimal recovery decisions during an airport constraint. We find that deterministic functions systematically overestimate potential disruption costs as well as optimal schedule recovery costs in high delay situations. Thus, an optimisation based on stochastic costs outperforms the deterministic approach by up to 15%, as it reveals ‘hidden’ downstream recovery potentials. This results in different slot allocations and in fewer passengers missing their connections.
... Beatty et al. (1999) and AhmadBeygi et al. (2008) used delay trees to track how delays propagate within an airline's network. Fricke and Schultz (2009) analyzed the individual inbound delay's impact on the turnaround process duration and stability. Kafle and Zou (2016) proposed an econometric model to analyze delay propagations. ...
En-route congestion causes delays in air traffic networks and will become more prominent as air traffic demand will continue to increase yet airspace volume cannot grow. However, most existing studies on flight delay modeling do not consider en-route congestion explicitly. In this study, we propose a new flight delay model, Multi-layer Air Traffic Network Delay (MATND) model, to capture the impact of en-route congestion on flight delays over an air traffic network. This model is developed by a data-driven approach, taking aircraft tracking data and flight schedules as inputs to characterize a national air traffic network, as well as a system-level model approach, modeling the delay process based on queueing theory. The two approaches combined make the network delay model a close representation of reality and easy-to-implement for what-if scenario analysis. The proposed MATND model includes 1) a data-driven method to learn a network composed of airports, en-route congestion points, and air corridors from aircraft tracking data, 2) a stochastic and dynamic queuing network model to calculate flight delays and track their propagation at both airports and in en-route congestion areas, in which the delays are computed via a space–time decomposition method. Using one month of historical aircraft tracking data over China’s air traffic network, MATND is tested and shows to give an accurate quantification of delays of the national air traffic network. “What-if” scenario analyses are conducted to demonstrate how the proposed model can be used for the evaluation of air traffic network improvement strategies, where the manipulation of reality at such a scale is impossible. Results show that MATND is computationally efficient, well suited for evaluating the impact of policy alternatives on system-wide delay at a macroscopic level.
... Thereby, the turnaround of each aircraft a ∈ A is defined by scheduled start and finishing times SIBT a and SOBT a , which are adopted from the flight plan. It consists of a network of sub-processes P , where each sub-process i ∈ P is characterized by the related aircraft (RA i = a), has a variable starting time s i and a duration D i which corresponds to the 80%-quantile of a statistically-fitted time distribution [11]. Links between turnaround sub-processes are determined in the precedence matrix P M ij ⊆ P × P . ...
The SESAR ATM Master Plan describes the goal to fully integrate airports into the ATM network, such that airspace user operations are facilitated and related user costs are reduced. The corresponding flight prioritization mechanisms of the user-driven prioritization process are in the process of being validated at several airports across Europe. This article studies the benefits of the underlying concept of tactical ATFCM slot swapping in relation to resource-constrained turnaround management of an airline. A case study at Frankfurt airport analyses different situations in which the airport is expected to operate with reduced capacity, such that a local hub carrier can prioritize its arrival (and departure) flights. Results indicate that arrival slot swapping in constraints is very efficient as long as fixed departure flights of the same aircraft obtain no critical delays for the downstream network. In our case study, such critical delays for the network occur at around 60 minutes of departure delay, such that flights which are assigned with higher delays require the additional flexibility of departure slot swapping in order to achieve significant cost reductions. We further find that optimal delay margins, which are calculated with our approach, suffice for the confidential communication of flight priorities, such that complex scoring and credit trading systems might be omitted. In exchange, we propose a secondary trading scheme, for which our model can define efficient slot prices while considering the operational constraints of an airline.
... Later research noticed that turnaround buffer time is taken as a means to reduce propagated delays from previous flight legs. Estimation methods, including survival analysis, quantile regression, and Bayesian models, are used to analyze flight departure delay with delay propagation-related factors [10][11][12][13]. Beside the effect of delay propagation on turnaround, researchers also notice this effect when a flight is airborne. ...
... In this paper, we use the CumDepDelay for DelayDep model and CumArrDelay for DelayArr model to present the departure and arrival congestion, respectively [11]. CumDepDelay and CumArrDelay respectively measure the departure and arrival delays generated in the airports during the past hour. ...
In recent years, flight delay costs the air transportation industry millions of dollars and has become a systematic problem. Understanding the behavior of flight delay is thus critical. This paper focuses on how flight delay is affected by operation-, time-, and weather-related factors. Different econometric models are developed to analyze departure and arrival delay. The results show that compared to departure delay, arrival delay is more likely to be affected by previous delays and the buffer effect. Block buffer presents a reduction effect seven times greater than turnaround buffer in terms of flight delays. Departure flights suffer more delays from convective weather than arrival flights. Convective weather at the destination airport for flight delay has a greater impact than at the original airport. In addition, sensitivity analysis of flight delays from an aircraft utilization perspective is conducted. We find that the effect of delay propagation on flight delay differs by aircraft utilization. This impact on departure delay is greater than the impact on arrival delay. In general, specific to the order of flights, the previous delay increases the impact on flight on-time performance as a flight flies a later leg. Buffer time has opposite effects on departure and arrival delay, with the order increasing. A decrease in buffer time with the order increasing, however, still has a greater reduction effect on departure delay than arrival delay. Specific to the number of flights operated by an aircraft, the more flights an aircraft flies in a day, the more the on-time performance of those flights will suffer from the previous delay and buffer time generally.
... Those few models which acknowledge the complexity of ground operations, do so for one individual turnaround or for individual resources on a rather microscopic level. Fricke and Schultz (2009) describe the most critical turnaround processes with stochastic time distributions and find that turnaround performance depends on the amount of arrival delay. Kuster et al. (2009) incorporate alternative options for some turnaround subprocesses into their scheduling model to reduce total ground time. ...
... Stochastic process durations have been analysed and case-sensitive probability density functions were determined in prior studies (Fricke and Schultz, 2009;Oreschko et al., 2012;Schultz et al., 2013). From these distributions, mean values are adopted for the respective aircraft type, airport and flight characteristics and used as deterministic parameters in the model. ...
Most airlines have established integrated hub and operations control centers for the monitoring and adjustment of tactical operations. However, decisions in such a control center are still elaborated manually on the basis of expert knowledge held by several agents representing the interests of different airline departments and local stakeholders. This article studies a concept which incorporates the situational awareness gained by airport-collaborative decision making into an airline-internal decision support system, such that it integrates all available schedule recovery options during aircraft ground operations. The developed mathematical optimization model is an adaptation of the Resource-Constrained Project Scheduling Problem (RCPSP) and incorporates key features from turnaround target time prediction, passenger connection management, tactical stand allocation and ground service vehicle routing into the airline hub control problem. The model is applied in a case study consisting of 20 turnarounds during a morning peak at Frankfurt airport. Schedule recovery performance (resilience) is analyzed for a set of key performance indicators within multiple scenario instances which contain different resource availability and aim at solving various arrival delay situations. Results highlight that a minimization of tactical cost concurrently reduces average departure delay for flight and passengers while recovery performance is substantially affected when some options are not included in the evaluation process. Thus, our concept provides airlines with an optimization approach for constrained airport resources so that total cost and delay resulting from schedule deviations are reduced, which may benefit strategic schedule planning and improve predictability of operations for local collaborators, such as airport, ground handlers and ATM performance.
