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Congestion as a consequence of the rapidly growing air traffic is one of the outstanding problems of the air transportation industry. Traffic impediment rates, having an increasing acceleration while the airport capacities have been kept constant due to several reasons, cause problems such as arrival/departure delays, schedule interruptions, cancellations and customer dissatisfaction. In this paper, the author aims to study transitioning from a single-hub air transportation system to a multi-hub infrastructure via Monte Carlo simulation.

The current hub has reached its capacity limits for long so that the growth potential of the air transportation has been affected adversely. One of the possible remedies suggested by authorities, professionals and academics was to transform the air transportation infrastructure into a multi-hub setting. Current air traffic of the country was modeled by means of simulation. Airport capacities and performances are simulated and analyzed under different scenarios considering a potential alternative hub along with the central one. Possible delays in both hubs are studied in case of moderately increasing traffic congestion.

As a result, decreased delay levels in the central hub are observed, whereas no delays are experienced in the potential one in all the scenarios.

This study, proposing to organize the national and international air traffic of the country while harmonizing the delay rates and increasing the passenger satisfaction, is to contribute significantly to the aviation sector companies, airliners and airport operators by shedding light on the imminent capacity issues air transportation industry is going to face.

This study aims to evaluate the network robustness of major Asian airlines and to explore which airport types have the greatest impact on robustness. We also analyze airports’ specific brokerage roles and their impacts on the robustness of the entire air route network. We select 10 major Asian full-service airlines that operate the main passenger terminals at the top-ranked hub airports in Asia. Data is collected from the Official Airline Guide passenger route dataset for 2017. The results of the network robustness analysis show that Air China and China Eastern Airlines have relatively high network robustness. In contrast, airlines with broader international coverage, such as Japan Airlines, Korean Air, and Singapore Airlines have higher network vulnerability. The measure of betweenness centrality has a greater impact on the robustness of air route networks than other centrality measures have. Furthermore, the brokerage role analysis shows that Chinese airports are more influential within China and Asia but are less influential globally when compared to other major hub airports in Asia. Incheon International Airport, Singapore Changi Airport, Hong Kong International Airport, and Narita International Airport play strong “liaison” roles. Among the brokerage roles, the liaison role has a greater impact on the robustness of air route networks.

This study aims to evaluate the network robustness of major Asian airlines and to explore which airport types have the greatest impact on robustness. We also analyze airports’ specific brokerage roles and their impacts on the robustness of the entire air route network. We select 10 major Asian full-service airlines that operate the main passenger terminals at the top-ranked hub airports in Asia. Data is collected from the Official Airline Guide passenger route dataset for 2017. The results of the network robustness analysis show that Air China and China Eastern Airlines have relatively high network robustness. In contrast, airlines with broader international coverage, such as Japan Airlines, Korean Air, and Singapore Airlines have higher network vulnerability. The measure of betweenness centrality has a greater impact on the robustness of air route networks than other centrality measures have. Furthermore, the brokerage role analysis shows that Chinese airports are more influential within China and Asia but are less influential globally when compared to other major hub airports in Asia. Incheon International Airport, Singapore Changi Airport, Hong Kong International Airport, and Narita International Airport play strong “liaison” roles. Among the brokerage roles, the liaison role has a greater impact on the robustness of air route networks.

Supply chains deliver goods and services between shippers and receivers, covering collection, transportation, distribution as well as their handling and storage in between. In particular, transportation services are carried out by different transport modes. In some modern supply chains, different categories of air cargo carriers – combinations, freighter-only, and/or integrators – provide critical transport services.

This chapter develops a methodology for estimating the performance of supply chains served by an air cargo carrier network. The methodology is based on indicators of infrastructure use, technical/technological level, operational factors, economic factors, and environmental performance. This proposed methodology is applied to estimate performance of supply chains served by an integrated air cargo carrier – FedEx Express – operating a single hub in the US domestic air network. Results indicate that the methodology may be useful for estimation of overall supply chain performance under the condition that relevant data are available.

This chapter reviews the effects of air transport liberalization, and investigates the roles played by airport-airline vertical arrangements in liberalizing markets. Our investigation concludes that liberalization has led to substantial economic and traffic growth. Such positive outcomes are mainly due to increased competition and efficiency gains in the airline industry, and positive externalities to the overall economy. Liberalization allows airlines to optimize their networks, and thus may introduce substantial demand and financial uncertainty to airports. Vertical arrangements between airlines and airports may offer a wide range of benefits to the parties involved, yet such arrangements could also lead to airline entry barriers which reduce the effects of liberalization. Three approaches have been developed to model the effects of liberalization in complex market conditions, which include the analytical, econometric and computational network methods. These approaches should be selectively utilized in policy studies on liberalization.

With significant changes in the aviation industry, various airport–airline arrangements have been formed to achieve alternative objectives. However, no consensus has been reached on such arrangements’ economic effects and the associated optimal public policy. This chapter aims to provide an interpretive review of the common types of airport–airline arrangements, the different modeling approaches used and key conclusions reached by recent studies. Our review suggests that airport–airline arrangements can take diverse forms and have been widely used in the industry. They may allow the airport and its airlines to internalize demand externality, increase traffic volume, reduce airport investment risks and costs, promote capacity investment, enhance service quality, or simply are a response to the competition from other airport–airline chains. On the other hand, such vertical arrangements, especially for those exclusively between airports and selected airlines, could lead to collusive outcomes at the expenses of non-participating organizations. The effects of such arrangements are also significantly influenced by the contract type, market structure and bargaining power between the airport and airline sectors. While case by case investigations are often needed for important economic decisions, we recommend policy-makers to promote competition in the airline and airport segments whenever possible, and demand more transparency or regulatory reporting of such arrangements. Policy debates and economic studies should be carried out first, before intrusive regulations are introduced.

The expected learning outcomes are to understand the complexities involved in the integration of two carriers with different business strategies and approaches, the merger of two brands with distinct personas and identities and the confluence of two different cultures; figure out the strategic options in front of the Tata Group and how it can deal with various macro- and micro-level business challenges, defy the financial hiccups and manoeuvre the operational complexities to accomplish mission Vihaan.AI; and develop a pragmatic approach to macro and micro business environmental scanning for making strategic business decisions.

In November 2022, Tata Group, the salt to software conglomerate, announced the merger of Air India (AI) and Vistara. This would lead to the formation of the full-service airline under the brand name “Air India”. The obvious reason behind this was the higher recognition, salience and recall of the brand AI as compared with Vistara in the global market. The Tata Group envisaged the brand AI to be a significant international aviation player with the heritage, persona and ethos of the brand Vistara in the renewed manifestation of AI. To realise these goals, Tata Group laid down an ambitious plan called “Vihaan.AI”, which was aimed at capturing a domestic market share of 30% by 2027.

This case study can be taught as part of undergraduate- and postgraduate-level management programmes.

Teaching notes are available for educators only.

CSS 11: Strategy.

There is a strong academic and professional interest in the changing business model of LCCs in Europe. Recently, even Ryanair which is often considered a European LCC role model has departed from the point-to-point paradigm by offering transfers within its own network. We first provide a general overview of recent changes in the business model of airlines that used to be categorized as LCCs. We then add to existing studies on LCC network strategies toward building connections. While we distinguish different approaches to accommodate transfer passengers, our analysis focuses on mesh networks as an airline network topology other than hub-and-spoke networks to provide online connections. A schedule analysis of Ryanair’s direct and indirect services at its base at Porto airport exemplifies that a mesh network might allow LCCs to go beyond stand-alone operations to become network carriers without requiring a complete transition of the generic LCC business strategy.

Low cost carriers (LCCs) have become one of the most significantly growing parts of the airline servicing market. This new player has redesigned the whole airline industry, which was previously led by the national/international full-service airline companies. Considering such advancements, the hub locations of LCCs became an important issue than ever before. Within this concept, a guiding framework is developed for an LCC company, which is in search of a new hub airport location within Turkey to satisfy the demand and attract new passengers.

An interval-valued intuitionistic fuzzy (IVIF) sets based weighted aggregated sum product assessment (WASPAS) and multi-objective optimization by ratio analysis (MULTIMOORA) methods are developed for decision-making processes.

Five airport locations are evaluated using the developed method. Results showed that in determining hub locations for LCCs, potential number of passengers of the city, airport quality and the number of hotels within the city are obtained as the three most important criteria among 12 evaluation criteria. The best location for the LCC company is determined as Antalya Airport.

To apply the proposed method to a different set of alternatives, data gathered on comparing location of alternatives from experts should be updated.

Proposed hybrid framework is presented as the first time in the literature as a decision-making tool. In order to validate framework's applicability, efficiency and effectiveness, a comparison and a sensitivity analysis are conducted at the end of the study.

This chapter considers time-differentiated airport noise surcharges that occur in addition to general noise fees at an airport. In practice, an essential problem of such surcharges may consist of setting the price for a social policy goal, such as airport noise reduction, by shifting a number of critical flights away from sensitive times-of-day in the presence of an additional, competing economic policy goal in terms of fostering the network hub function and connectivity of that airport. In such a case, additional noise surcharges aim at balancing the socioeconomic noise costs against economic prosperity, to achieve a net benefit for society by inducing a particular airline scheduling behavior, such as shifting non-hub-relevant flights only. As a result, they differ from the well-known economic concepts for the internalization of externalities. We address this problem by offering a shift from an economic welfare view to a business administration perspective with the airlines as stakeholders, in order to describe the different rationales that need to be accounted for when searching for a pricing scheme that achieves one of the distinct steering effects in terms of airline scheduling behavior. In addition, we offer a tentative, generic guideline to determine the appropriate dimension of time-differentiated noise surcharges depending on the steering effect.