The impact of security oversight on air cargo price and demand

This paper examines the effect of security oversight on air cargo price and demand. We exploit variations in security oversight instituted by the International Civil Aviation Organization (ICAO). We estimate a simultaneous equation model using proprietary operations data from a major airline in South Korea over the period 2009–2013. This study explores the shipping-charge behavior of a service provider through a modeling approach that considers air cargo security. Our findings show that security oversight increases air cargo demand, controlling for the effect of price. Improving security measures increases the air cargo price, but the magnitude of this increase is small. Our results should help policymakers gauge the benefit of improved security and help airlines design an effective model to determine future air cargo shipping charges under high uncertainty to mitigate short- and long-term financial risks.

Airline strategies during the pandemic: What worked?

An examination is conducted of airline strategies during the covid-19 pandemic using data from the United States. Our findings show that airlines pursued diverse strategies in terms of route entry and retention, pricing, and load factors. At the route level, a more detailed examination is conducted of the performance of a middle-seat blocking strategy designed to increase the safety of air travel. We show that this strategy (i.e., not making middle seats available to passengers) likely resulted in revenue losses for carriers, an estimated US $3,300 per flight. This revenue loss provides an indication as to why the middle seat blocking strategy was discontinued by all US airlines despite ongoing safety concerns.

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

Purpose>One 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/approach>One 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.Findings>The 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/value>The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

Relationship of Covid-19 Pandemic on Performance and Internal Operation of The Airline

. Air transport business is challenged to oversee their performance and operations to preserve their business presence within the COVID-19 widespread. It is fundamental to distinguish the suitable performance and operation and their relationship with the pandemic. In this way we used the indicators to employ a systematic literature review and experts' point of view to select and group suitable indicators to address the literature gap. Twenty performance and internal operations indicators are identified and redefined from the review. The Delphi method's result suggests eight indicators categorized in airline performance indicators and seven indicators categorized in internal operation indicators. We investigate the relationship of COVID-19 pandemic on the selected indicators using SmartPLS based on fifty-two weeks performance, and operation report of one of full-service airline started from 08 March 2020 until 28 February 2021. It can be deduced that the COVID-19 pandemic has a significant and negative influence on the airline's performance. This study fills the gap by synthesizing and creating suitable and comprehensive performance and operation indicators of air transport carriers in a pandemic situation and their relationships. Finally, this study provides an invaluable point for analyzing the air transport carrier industries in a pandemic to maximize performance through profitability and load factors indicators.

Outsmarting Human Design in Airline Revenue Management

The accurate estimation of how future demand will react to prices is central to the optimization of pricing decisions. The systems responsible for demand prediction and pricing optimization are called revenue management (RM) systems, and, in the airline industry, they play an important role in the company’s profitability. As airlines’ current pricing decisions impact future knowledge of the demand behavior, the RM systems may have to compromise immediate revenue by efficiently performing price experiments with the expectation that the information gained about the demand behavior will lead to better future pricing decisions. This earning while learning (EWL) problem has captured the attention of both the industry and academia in recent years, resulting in many proposed solutions based on heuristic optimization. We take a different approach that does not depend on human-designed heuristics. We present the EWL problem to a reinforcement learning agent, and the agent’s goal is to maximize long-term revenue without explicitly considering the optimal way to perform price experimentation. The agent discovers through experience that “myopic” revenue-maximizing policies may lead to a decrease in the demand model quality (which it relies on to take decisions). We show that the agent finds novel pricing policies that balance revenue maximization and demand model quality in a surprisingly effective way, generating more revenue over the long run than current practices.

The omnidirectional runway with infinite heading as a futuristic runway concept for future free route airspace operations

Purpose>The purpose of this paper is to create and analyze the effectiveness of a new runway system, which is totally created for the future free route operations.Design/methodology/approach>This paper researches and analyses the new generated runway concept with the fast time simulation method. Fuel consumption and environmental effect of the new runway system are calculated based on simulation results.Findings>According to different traffic density analyses the Omnidirectional Runway with Infinite Heading (ORIH) reduced fuel consumption and CO2 emissions up to 46.97%. Also the total emissions of the ORIH concept, for the hydro carbon (HC), carbon monoxide (CO) and nitrogen oxides (NOx) pollutants were lower than the total emissions with the conventional runway up to 83.13, 74.36 and 51.49%, respectively.Practical implications>Free route airspaces bring many advantages to air traffic management and airline operations. Direct routes become available from airport to airport thanks to free route airspace concept. However, conventional single runway structure does not allow aircraft operations for every direction. The landing and take-off operations of a conventional airport with a single runway must be executed with only two heading direction. This limitation brings a bottleneck direct approach and departure route usage as convenient with free route airspace concept. This paper suggests and analyzes the omnidirectional runway with infinite heading (ORIH) as a solution for free route airspace.Originality/value>This paper suggests a new and futuristic runway design and operation for the free route operations. This paper has its originality from the suggested and newly created runway system.

Airline OR Innovations Soar During COVID-19 Recovery

The Airline Group of the International Federation of Operational Research Societies (AGIFORS) held four conferences during May to July 2021 that focused on how COVID-19 was impacting and reshaping the airline industry. Dozens of airline representatives from around the world spoke about how fundamental changes in passenger demand and booking patterns are reshaping the airline industry and driving innovation and research needs. Customers are booking much closer to departure and are canceling or exchanging their tickets more frequently than before COVID-19. Volatility in demand has increased as travel restrictions change and borders reopen. Consequently, greater uncertainty in demand forecasts used as inputs to optimization algorithms is motivating the need for new approaches. Revenue management and scheduling departments are innovating how they predict market sizes and exploring ways to use new data sources or historic bookings in forecasting models. Scheduling and operations departments are making many more flight-cancellation and equipment-swap decisions one to three days from flight departure, which is changing the role of recovery planning. New urgency exists within crew to design duties and rosters that are robust to ever-evolving schedules. Across functional areas, the increasing emphasis is to develop integrated solutions that jointly optimize schedules, crew pairings, and crew rosters for demand forecasts that are uncertain at the time rosters are published. This paper describes how these changes are reshaping the airline industry during COVID-19, explains why short lead times for bookings and uncertainty in demand volumes are expected to remain after COVID-19, and describes how the airline industry is innovating and developing new operations research approaches for handling uncertain and volatile demand. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Grey clustering of the variations in the back-to-front airplane boarding method considering COVID-19 flying restrictions

PurposeThe airline industry has been significantly hit by the occurrence of the new coronavirus SARS-CoV-2, facing one of its worst crises in history. In this context, the present paper analyses one of the well-known boarding methods used in practice by the airlines before and during the coronavirus outbreak, namely back-to-front and suggests which variations of this method to use when three passenger boarding groups are considered and a jet bridge connects the airport terminal with the airplane.Design/methodology/approachBased on the importance accorded by the airlines to operational performance, health risks, and passengers' comfort, the variations in three passenger groups back-to-front boarding are divided into three clusters using the grey clustering approach offered by the grey systems theory.FindingsHaving the clusters based on the selected metrics and considering the social distance among the passengers, airlines can better understand how the variations in back-to-front perform in the new conditions imposed by the novel coronavirus and choose the boarding approach that better fits its policy and goals.Originality/valueThe paper combines the advantages offered by grey clustering and agent-based modelling for offering to determine which are the best configurations that offer a reduced boarding time, while accounting for reduced passengers' health risk, measured through three indicators: aisle risk, seat risk and type-3 seat interferences and for an increased comfort for the passengers manifested through a continuous walking flow while boarding.