Airline Costs Research Articles

Costs for passengers and airlines due to the significant delays and other irregularities at European airports in the 2022 summer season

In the 2022 summer season, severe flight delays, lost luggage and other irregularities were recorded at a number of European airports. Staff cuts during the COVID-19 pandemic, a fairly strong recovery in the aviation sector in 2022 and resulting staff shortages were identified as the main causes. However, some airports (e.g. in Southern Germany) appear to have managed these problems better than others. Such severe delays and other irregularities in air transport cause economic damage for different stakeholders, especially for airport operators, airlines, the (federal) police, ANSPs (air navigation service providers), ground-handling and security service companies, as well as for the travelers themselves. In this paper we evaluate the key causes for, and economic effects of the recorded irregularities for the passengers and airlines. As a first step, literature on the economic impact of delays in air transport is analyzed. In a second step, available data is used to quantify the delay situation in the summer period of 2022 for selected flight routes and airports. After this, the value of time damage is estimated for flight passengers based on the selected flights. This is complemented by a delay cost estimation for the airlines serving the affected airports. The paper closes with recommendations for improvement.

Sustainable air transportation through the operational use of a social cost index

Air transportation is responsible for more than 10% of transportation related greenhouse gas emissions in the U.S. As policymakers and engineers search for methods to reduce aviation fuel consumption and the associated negative externalities, a metric used by aircrew on the flight deck may hold the key. The cost index metric and associated ECON speeds inputted into the flight management system control the aircraft's flying speed and ultimately the fuel consumed, and emissions produced. This study assesses the use of this operational tool to further green air transport production in U.S. domestic operations. Data from four major airline Boeing 737 NG routes are analyzed to estimate the effect of each carrier's cost index and ECON speeds on fuel consumption and emissions. Then a social cost index, which incorporates environmental costs, is estimated and tested using a Boeing 737 NG simulator across identical routes to determine the changes in fuel consumption and emissions. On average, we find that for every one unit decrease in an airline's cost index they reduce their fuel consumption by 5.37 lbs. per hour. Further, the average impact of implementing a social cost index is estimated to save nearly 22 gallons of jet fuel and reduce CO2 emissions by 453 lbs. per flight hour. These fuel and emissions reductions are estimated to generate a penalty of 1 min and 43 s on average per flight hour. Overall, we find that airlines can substantially reduce fuel consumption and emissions with minimal impact to operations via implementation of a social cost index.

Factors Affecting the International Flight Delays and Their Impact on Airline Operation and Management and Passenger Compensations Fees in Air Transport Industry: Case Study of a Selected Airlines in Europe

This paper aims to analyze the causes of flight delays of a selected airline operating in Europe and identify potential risks and reasons for delays in air transport, which carry risk, especially in connection with passenger dissatisfaction and the resulting need to pay compensation for delayed flights, according to EU law, which brings significant financial costs for airlines. Data collected from the years 2013–2019 have been used for the purposes of this article, including data on the duration and causes of the delays and the characteristics of individual flights, such as the flight type, aircraft type, time of departure, aircraft capacity, and the load factor. Multidimensional statistics methods have been applied for data processing, namely tests of independence and correspondence analysis. Dependencies have been presented in graphical form using correspondence maps. The analysis shows that the total share of delayed flights of the company in question does not increase over the mentioned period of time. The analysis furthermore proved that higher capacity aircraft were rather prone to longer delays. The share of delayed charter flights declined slightly over the period under review, while the number of delays caused by airline operating reasons, aircraft clearance by supplier companies, technical maintenance and aircraft defects, operating procedures, and crew flight standards and airport restrictions is increasing. The analysis also shows that the delays propagated by the delays of the previous flights are becoming more frequent, with the exception of the year 2019. In the last pre-COVID year, air traffic control could boast about a positive trend regarding the number of produced delays over the years, contributing to greater air passenger satisfaction and airline service quality. Due to the gradual revival of air transport after the COVID pandemic lockdowns, the topic of flight delays, passenger customer satisfaction and financial costs for the resulting delays is once again an actual issue for air management and operation.

Population-Aware Sequential Flight Path Optimization for Low-Noise and Low-Fuel Consumption Departure Trajectory

With the increase in air traffic over the past decades, the reduction of aircraft noise is one of the major challenges facing stakeholders. Flight operating conditions that decrease noise may possibly increase the fuel consumption of aircraft, which is an important factor in airline cost management. In this paper, we propose a methodology to support flight path planning with the aim of optimizing both perceived noise and fuel consumption. We decompose an aircraft flight trajectory into surface and altitude paths to model relevant air transportation constraints. The shortest path for the surface projection is found using Dubins path method and an improved algorithm, which considers guide points according to the flight destination, runway angles, spatial separation of aircraft near the airport, population distribution, and steering motion. The altitude path is optimized for low-perceived noise and low-fuel consumption, which is determined by solving the longitudinal governing equations of motion of flight using the distance computed from this surface path. A modified nondominated sorting genetic algorithm II for discrete optimization is developed to obtain Pareto fronts of the optimum altitude paths with reduced computational effort. The methodology is demonstrated by simulating flights departing from Hong Kong International Airport to two compulsory air-traffic-service reporting points. The results are then compared with Quick Access Recorder data and Standard Instrument Departure (SID) tracks. Although certain factors in air transportation that affect departure path planning, such as weather patterns and air traffic mix, are not considered in this method, the resulting surface path exhibits a close similarity with SID tracks. The resulting Pareto fronts of the altitude path exhibit reductions in fuel consumption and perceived noise levels. The tradeoffs between fuel consumption and perceived noise levels are also discussed based on the relevant flight physics for the different routes.

Coordinating aircraft maintenance routing and integrated maintenance staffing and rostering: a Stackelberg game theoretical model

There is an interdependence between operational-based flight delay aircraft maintenance routing problem (AMRPD) and maintenance staffing problem (MSP). Furthermore, MSP and maintenance rostering problem (MRP) are interdependent. In the literature, most of the studies only consider a single interdependence and neglect the other one. This results in difficulties in implementing the optimal plan constructed by each problem, leading finally to an increase in the operational cost of airlines and maintenance providers. Therefore, the main objective of this study is to investigate these interdependences simultaneously. This is accomplished by developing a Stackelberg game model (LFSGM), in which leader and follower are the airlines and maintenance providers, respectively. This LFSGM is modelled as a bi-level mixed integer linear programming (MILP) model. The upper-level is represented by the AMRPD, while the integrated maintenance staffing and rostering (IMSR) functions as the lower-level. The solution of the LFSGM is obtained from an iterative algorithm. The viability and superiority of the LFSGM are demonstrated by presenting a case study of a leading airline in the Middle East serviced by four maintenance providers. The results reveal a significant cost saving for airline and maintenance providers.

The impact of engine standardization on the cost efficiency of airlines

Fleet standardization, which refers to the homogeneity and harmonization of the fleet in terms of the number of manufacturers and models, as a strategy has been deployed across many sectors, including airline operations. Given that engines are substantial aircraft components in terms of both capital and operating cost, representing nearly 50% of the aerospace aftermarket, we investigate whether – and to what extent – standardization strategies in the context of engines can improve cost efficiency of the airline industry. Using engine data of 12,305 aircraft and financial data of years with high (2013/14) and low (2016/17) fuel prices, we apply bootstrapped Data Envelopment Analysis (DEA) followed by random effects panel regression analysis to 84 airlines across the globe. Our quantitative findings are validated by qualitative stakeholder interviews. Our results suggest that both airframe and engine commonality impact on airline cost efficiency, but that engine cost effects are significantly larger in magnitude than standard airframe cost effects. In contrast to current management practices focusing exclusively on airframe commonality and tactical activities (such as renegotiating supplier and maintenance contracts), we demonstrate that an engine standardization strategy (through procurement, storage, retirement, maintenance and spare part optimization) improves the cost competitiveness and efficiency of airlines in an OEM servitized world.

Jet fuel demand hit by Omicron; recovery delayed until 2023/2024

Jet fuel demand hit by Omicron; recovery delayed until 2023/2024

A flexible mathematical model for crew pairing optimization to generate n-day pairings considering the risk of COVID-19: a real case study

PurposeOne of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day pairings can be generated in a single model. The flexibility in generating parings causes that the proposed model leads to better solutions compared to existing models. Another advantage of the model is minimizing the risk of COVID-19 by limitation of daily flights as well as elapsed time minimization. As airports are among high risk places in COVID-19 pandemic, minimization of infection risk is considered in this model for the first time. Genetic algorithm is used as the solution approach, and its efficiency is compared to GAMS in small and medium-size problems.Design/methodology/approachOne of the most complex issues in airlines is crew scheduling problem which is divided into two subproblems: crew pairing problem (CPP) and crew rostering problem (CRP). Generating crew pairings is a tremendous and exhausting task as millions of pairings may be generated for an airline. Moreover, crew cost has the largest share in total cost of airlines after fuel cost. As a result, crew scheduling with the aim of cost minimization is one of the most important issues in airlines. In this paper, a new bi-objective mixed integer programming model is proposed to generate pairings in such a way that deadhead cost, crew cost and the risk of COVID-19 are minimized.FindingsThe proposed model is applied for domestic flights of Iran Air airline. The results of the study indicate that genetic algorithm solutions have only 0.414 and 0.380 gap on average to optimum values of the first and the second objective functions, respectively. Due to the flexibility of the proposed model, it improves solutions resulted from existing models with fixed-duty pairings. Crew cost is decreased by 12.82, 24.72, 4.05 and 14.86% compared to one-duty to four-duty models. In detail, crew salary is improved by 12.85, 24.64, 4.07 and 14.91% and deadhead cost is decreased by 11.87, 26.98, 3.27, and 13.35% compared to one-duty to four-duty models, respectively.Originality/valueThe authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

Tactical runway scheduling for demand and delay management

PurposeAirports are limited in terms of capacity. Particularly, runways can only accommodate a certain number of movements (arrivals and departures) while ensuring safety and determined operational requirements. In such a constrained operating environment, any reduction in system capacity results in major delays with significant costs for airlines and passengers. Therefore, the efficient operation of airports is a critical cornerstone for demand and delay management of the whole air transportation system. Runway scheduling deals with the sequencing of arriving and departing aircraft at airports such that a predefined objective is optimized subject to several operational constraints, like the dependency of separation on the leading and trailing aircraft type or the runway occupancy time. This study aims to develop a model that acts as a tactical runway scheduling methodology for reducing delays while managing runway usage.Design/methodology/approachBy considering real airport performance data with scheduled and actual movements, as well as arrival/departure delays, this study presents a robust model together with an optimization algorithm, which incorporates the knowledge of uncertainty into the tactical operational step. The approach transforms the planning problem into an assignment problem with side constraints. The coupled landing/take-off problem is solved to optimality by exploiting a time-indexed (0, 1) formulation for the problem. The Binary Integer Linear Programming approach allows to include multi-criteria and multi-constraints levels and, even with some major simplifications, provides fewer sequence changes and target time updates, when compared to the usual approach in which the plan is simply updated in case of infeasibility. Thus, the use of robust optimization leads to a protection against tactical uncertainties, reduces delays and achieves more stable operations.FindingsThis model has been validated with real data from a large international European airport in different traffic scenarios. Results are compared to the actual sequencing of flights and show that the algorithm can significantly contribute to the reduction of delay, while adhering as much as possible to the operative procedures and constraints, and to the objectives of the airport stakeholders. Computational experiments performed on the case study illustrate the benefits of this arrival/departure integrated approach: the proposed algorithm significantly reduces weighted aircraft delay and computes efficient runway schedule solutions within a few seconds and with little computational effort. It can be adopted as a decision-making tool in the tactical stage. Furthermore, this study presents operational insights regarding demand and delay management based on the results of this work.Originality/valueScheduling arrivals and departures at runways is a complex problem that needs to address diverse and often competing considerations among involved flights. In the context of the Airport Collaborative Decision Making programme, airport operators and air navigation service providers require arrival and departure management tools that improve aircraft flows at airports. Airport runway optimization, as the main element that combines airside and groundside operations, is an ongoing challenge for air traffic management.

An Improved Artificial Neural Network Model for Flights Delay Prediction

With the limitation of air traffic and the rapid increase in the number of flights, flight delay is becoming more frequent. Flight delay leads to financial and time losses for passengers and increases operating costs for airlines. Therefore, the establishment of an accurate prediction model for flight delay becomes vital to build an efficient airline transportation system. The air transportation system has a huge amount of data and complex operation modes, which is suitable for analysis by using machine learning methods. This paper discusses the factors that may affect the flight delay, and presents a new flight delay prediction model. The five warning levels are defined based on flight delay database by using K-means clustering algorithm. After extracting the key factors related to flight operation by the grey relational analysis (GRA) algorithm, an improved machine learning algorithm called GRA — Genetic algorithm (GA) — back propagation neural network, GRA-GA-BP, is introduced, which is optimized by GA. The calculation results show that, compared with models before optimization and other two algorithms in previous papers, the proposed prediction model based on GRA-GA-BP algorithm shows a higher prediction accuracy and more stability. In terms of operation efficiency and memory consumption, it also has good performance. The analysis presented in this paper indicates that this model can provide effective early warnings for flight delay, and can help airlines to intervene in flights with abnormal trend in advance.

COST CLASSIFICATION AND ANALYSIS OF CONSUMER DEMAND TO OPTIMIZE AIR TRANSPORTATION AND ENSURE THE ECONOMIC STABILITY OF KAZAKHSTAN AIRLINES IN THE FACE OF PANDEMICS


 
 
 
 This article analyzes the costs of aviation companies in the context of the pandemic, including for the airline of the Republic of Kazakhstan. The classification of airline costs is considered. Cost classification and analysis of consumer demand, taking into account the epidemiological situation in different regions, are the basis for optimizing air transportation, reducing costs and ensuring the economic stability of the airline in the face of pandemics.
 
 
 

Environmental Impact and External Costs Associated with Hub-and-Spoke Network in Air Transport

Under the background of economic globalization, the air transport industry developed rapidly. It turns out that the city-to-city network has not been able to adapt well to the development of the society, and the hub-and-spoke network came into being. The hub-and-spoke network demonstrates the advantages of reducing the operating costs of airlines to keep a competitive advantage, and by maintaining the interests of airlines in the rapidly developing context. However, during the operation of aircrafts, they consume fuel and spew a great deal of harmful pollutants into the air, which has an adverse impact on the living environment. This paper explores the impact and external costs associated with hub-and-spoke network in air transport from an environmental perspective. With some mathematical models, we construct a hub-and-spoke network and take a quantitative study on the environmental impact of air transport. For calculating pollutant emissions, meteorological conditions were considered to revise the pollutant emission factors of the Engine Emissions Data Base (EEDB) published by International Civil Aviation Organization (ICAO). The environmental external costs measurement model is employed to calculate the externality of toxic gas and greenhouse gas (GHG). In order to make the study more convincing, two alternative networks are computed: hub-and-spoke network and city-to-city network. It is found that the hub-and-spoke network is associated with poorer environmental impact and environmental external costs because of the different network characteristics and the scale of the fleets. Therefore, under the general trend of green aviation, the environmental impact and environmental external costs associated with hub-and-spoke network in air transport provides a certain reference for airlines’ strategic decision-making.

Impact of digital technologies on airline operations

With more and more passengers choosing to fly by air, how to improve customer in-flight experience becomes an important issue for airlines. Digitalization has changed the way people enjoy their lives and it also has impacted on airline\’s business strategy. Thus, it is worth to investigate how digitalization impacts on passengers’ in-flight preference. Due to the different strategy, inflight entertainment (IFE) system has different application in low-cost airlines and full-service airlines. In order to balance the cost and revenue of airlines, a customer satisfied survey about IFE has been conducted to investigate how passengers value IFE when they travel by air. Since more than 1/3 of respondents thought IFE was an important element when they taking flights, it is meaningful to provide passengers with IFE systems to improve their satisfaction. Thus, based on passengers\’ needs, more types of movies and TV programs, personality functions, earphones, and other elements could be added to IFE systems to enhance customer in-flight experience.

Tactical Runway Scheduling for Demand and Delay Management

Airports are limited in terms of capacity. Particularly, runways can only accommodate a certain number of movements (arrivals and departures) while ensuring safety and determined operational requirements. In such a constrained operating environment, any reduction in system capacity results in major delays with significant costs for airlines and passengers. Therefore, the efficient operation of airports is a critical cornerstone for demand and delay management of the whole air transportation system. Runway scheduling deals with the sequencing of arriving and departing aircraft at airports such that a predefined objective is optimized subject to several operational constraints, like the dependency of separation on the leading and trailing aircraft type or the runway occupancy time. Scheduling arrivals and departures at runways is a complex problem that needs to address diverse and often competing considerations among involved flights. In the context of the Airport Collaborative Decision Making (A-CDM) programme, airport operators and air navigation service providers require arrival and departure management tools that improve aircraft flows at airports. Airport runway optimization, as the main element that combines airside and groundside operations, is an ongoing challenge for air traffic management. By considering real airport performance data with scheduled and actual movements, as well as arrival/departure delays, we present a robust model together with an optimization algorithm, which incorporates the knowledge of uncertainty into the tactical operational step. Our model has been validated with real data from a large international European airport in different traffic scenarios. Results are compared to the actual sequencing of flights and show that the algorithm can significantly contribute to the reduction of delay, while adhering as much as possible to the operative procedures and constraints, and to the objectives of the airport stakeholders. Computational experiments performed on the case study illustrate the benefits of this arrival/departure integrated approach.

Research on Location Selection Strategy for Airlines Spare Parts Central Warehouse Based on METRIC.

With the increased demands of airlines, it is important to study the location selection strategy for spare parts central warehouse in order to improve the allocation capacity of spare parts maintenance resources and reduce the operating costs of airlines. Based on the M/M/s/∞/∞ multiservice desk model and Multi-Echelon Technique for Recoverable Item Control (METRIC) theory, this paper proposes a spare parts supply strategy based on the spare parts pool network and establishes a location selection model for spare parts central warehouse. The particle swarm optimization (PSO) algorithm is used to iteratively optimize the location for spare parts central warehouse and adjust the location area of the central warehouse combining transportation facilities and geographical environment factors. Finally, the paper compares the operating results for multiple airlines in pooling and off-pooling states and verifies the effectiveness of the spare parts supply model and the advantages of cost control for airlines.

Research on airport multi-objective optimization of stand allocation based on simulated annealing algorithm

<abstract> <p>In this study, a multi-objective optimized mathematical model of stand pre-allocation is constructed with the shortest travel distance for passengers, the lowest cost for airlines and the efficiency of stand usage as the overall objectives. The actual data of 12 flights at Lanzhou Zhongchuan Airport are analyzed by application and solved by simulated annealing algorithm. The results of the study show that the total objective function of the constructed model allocation scheme is reduced by 40.67% compared with the actual allocation scheme of the airport, and the distance traveled by passengers is reduced by a total of 4512 steps, while one stand is saved and the efficiency of stand use is increased by 31%, in addition to the reduction of airline cost by 300 RMB. In summary, the model constructed in the study has a high practical application value and is expected to be used for airport stand pre-allocation decision in the future.</p> </abstract>

Analysis of airport weather impact on on-time performance of arrival flights for the Brazilian domestic air transportation system

Air traffic operations are significantly impacted by weather conditions. These external factors may impose operational constraints and generate demand-capacity imbalances, leading to reduced on-time performance, additional airline costs and inconveniences to passengers. Efficient management of such disruptions requires an understanding of the main causes of flight delays towards increasing their predictability. In this study, we investigate the impacts of airport surface weather conditions on the likelihood of flight delays for the Brazilian domestic air transportation system. We use historical flight schedule, on-time performance and weather data and estimate a logit model to analyze how different meteorological variables at the airport of destination affect the probability of a delayed arrival. We obtain empirical evidence for the impacts of low ceiling and visibility conditions, precipitation and wind gusts on the likelihood of arrival delays for the set of Brazilian airports analyzed.

The Early Impact of Covid-19 Pandemic on the Aviation Industry

The goal of the paper is to assess early economics impact of Covid-19 on the aviation industry as it is one of the hardest hit industries. Covid-19 has an unprecedented impact on the international passenger seat capacity and gross passenger operating revenues of airlines. In the first part, we look at the impact of the pandemic on the economy as a whole and subsequently look at the economic cost of airlines. Two main elements are driving this negative trend – travel restrictions and overall economic development. We are currently experiencing the tightest international travel restrictions in the modern era of flight transportation with the economies worldwide experiencing a greater decline in GDP than during the last 3 recessions. In the second part, we look at two out of many possible path scenarios which are likely to drive the trend: V-shaped path and U-shaped path. All pandemic outbreaks before followed V-shaped scenario but Covid-19 has paralysed the society for longer with stricter governmental measures and so U-shaped scenario has to be taken into account as the most likely outcome for the aviation industry during this pandemic.

Dynamic Model for Scheduling Crew Shifts

In regular as well as nonscheduled air transport, extraordinary situations occasionally occur, which may fundamentally disrupt the flight schedule. Fundamental disruptions of flight schedules affect not only passengers but also the airline. One of the areas that are negatively affected by the disruption is the crew plan. Due to extraordinary events, it happens that a flight is delayed, and the crew will not be at the destination airport at the prescribed time and the airline will not be able to assign it on further flights according to the original plan. Such situations can be resolved either by deploying any other available crew or by delaying the flight appropriately until the previously planned crew is available. Assigning a new crew entails additional costs for the airline, as it has to assign more flight staff than had been originally planned. Furthermore, delayed flights lead to paying passengers financial compensation, incurring additional costs for airlines. Therefore, it is important that the airline is able to resolve any irregularity situations so that the additional costs incurred to deal with the irregularity situations are kept at a minimum. The paper presents one possible approach, a mathematical model that can be used to solve such a situation. The presented mathematical model may be the basis for the decision support system of the operations center worker who is responsible for the operational management of flight crews. The model will primarily aim at smaller airlines that cannot afford expensive software and often rely on manual solutions. However, a manual solution may not always be the best, as the operator, who plans the processes, may not consider all the constraints. Another important factor that makes the decision processes more difficult is that it is usually necessary to decide in a short period of time. The solution proposed in this paper will allow the operator to make a quick decision that will also be the most advantageous for the airline. This is because the proposed method is an exact approach, which guarantees finding the optimum solution. In this article, we are only dealing with pilot crews.

Understanding airline price dispersion in the presence of high-speed rail

This paper examines the price dispersion among China's “Big Three”, namely, Air China, China Eastern and China Southern in the presence of high-speed rail (HSR). It has been found that HSR is positively and significantly associated with airline price dispersion on the long-haul routes, which may suggest that the presence of HSR can facilitate airline cooperation in setting prices and outputs, thereby leading to greater price dispersion. However, on the short-haul routes where HSR is highly substitutable, the HSR competition effect dominates, and smaller price dispersion is observed. All the market structure and competition variables included in this study support the conclusion that price dispersion is greater in more concentrated and more densely travelled markets. The contribution of airline cost to price dispersion is limited.