The global air transport industry plays a fundamental role in facilitating economic growth, this is especially so in developing countries, tourism, commerce and linking countries and regions.1 Air transportation is defined as the transportation of persons, goods, property or postal mail by air. It comprises two primary segments, the transport of passengers as well as the carriage of air cargo consignments. Each segment of the industry has its own unique characteristics, yet the two are difficult to consider in isolation, therefore air transportation is often referred to as a “bi-polar” industry.2 In 2016, the world’s airlines carried around 3.79 billion passengers and 52.6 million tonnes of air cargo.3 Furthermore, the air transport industry is regarded as being indispensable for tourism,4 which is also a strong driver of economic growth.5‒7 In 2016, around 55 per cent of all overnight visitors travelled to their destination by air. 4 In the global airline industry, passenger airlines have defined and implemented two principal business models: the full-service network carrier (FSNC) and the low-cost carrier (LCC) models.8,9 Whyte & Lohmann10 note that a ‘business model is a conceptual structure or plan that defines how a company conducts its business’. The full-service network carrier’s business model forms the focus of the present study.

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The role of the airbus A380-800 aircraft in a full-service network airline’s commercial operations and route network design: the case of Thai airways international

The research objective of this thesis is to develop an innovative airline fleet planning concept that is capable to consider the long-term stochastic nature of air travel demand while generating meaningful results in reasonable computation times. The proposed methodology aims to identify robust fleets, in terms of profit generating capability across a long-term planning horizon under stochastic demand, through the adoption of a portfolio of fleets (each of different size or composition) and a three-step modeling framework. The three models involve the simulation and sampling of stochastic demand using the mean reverting Ornstein-Uhlenbeck process, iteration over an optimization model that optimally allocates each fleet from the portfolio given the demand sample values, and a scenario generation model that generates scenarios across the planning horizon. A case study is presented and serves as proof of concept.

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Robust fleet planning under stochastic demand

With the first batch of used A380 entering the market the question arises if there is a second-hand market for this type of aircraft. Aircraft prices – for new and used aircraft – are generally subject to significant discounts and confidentiality, which impedes the development and validation of scientific quantification methods. In this study a fleet-planning-based approach is applied to an exemplary airline to determine the economic viability of introducing a used A380 into an airline fleet. Specifically, a parameter study with varying prices of used A380 is performed. The results indicate that, when compared to a new A380, the discounted used variant can be a reasonable alternative. However, when compared to a younger aircraft type, even a strongly discounted A380 is more cost-efficient only, if not operated for more than a few years. Major cost drivers of the used variant are MRO and fuel cost. Possible operational scenarios for used A380 are pictured and an outlook for further research is given.

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Prospects of the A380 second-hand market

Hitherto, sustainably profitable low-cost operations can only be observed among shortto medium-haul airlines. Even though various carriers have launched attempts to extend the low-cost carrier (LCC) business model to longhaul flights (LHF, see the subsequent chapter for a definition), most of them failed before flight operations were initiated or went bankrupt after three to five years if flight operations started. However, the frequency with which new low-cost LHF airlines are founded is still considerable (see Tab. 1). Entrepreneurs as well as established airlines seeking new growth paths are among those in the industry that follow what seems to be a trial-and-error process in launching and operating LCC LHF airlines. This is not particularly surprising, since little systematic guidance on preconditions for and design options of a LCC LHF business model have been advanced. Rather, the scholarly literature has given only scant attention to the challenge of adapting the low-cost model to LHF, and the question of whether the low-cost long-haul model is economically viable has not been unanimously answered yet.

Can low-cost long-haul flight operations be profitable?

Measured in volume approximately 80 % of world trade is carried at sea and with just as many different actors the shipping industry acts close to a perfect market. The highly volatile nature of the industry with unexpected market fluctuations is the basis for the major decisions shipping companies are making. Especially the fleet size and mix problem in a strategic setting involving fleet changes during several planning periods as a company growth policy. This decision is therefore highly dependent on correct timing for those who want to succeed and an introduction to the shipping industry is given to state these properties. In this thesis various optimization models solving the fleet size and mix problem are presented where the best suited model structure related to the topic is chosen. This model is of deterministic nature, meaning that all input values are known, and based upon predefined routes. The decision regarding the fleet composition during several planning periods is aiming at determining an optimal fleet for a given market. The validity of the results solely relies on input data, which is highly uncertain into an unknown future. The predictions need to coincide with the real life development in order for the results to maintain its validity. Two different trades are used as cases, solved with the models presented. Some input parameters are changed and the differences are investigated. The main findings imply that only relative small changes of the input parameters resulted in very different decisions. The related loss of making the wrong decision is observed in the region of 100  200 million USD during three years. This large loss potential and the uncertainty related to the input parameters leads to a need for a method minimizing these effects. An approach is developed to treat uncertainties minimizing the losses by finding a robust fleet capable of handling a large set of generated future scenarios, called the Scenario Algorithm. The approach is divided into three main steps; the scenario generating step where development are based on historical fluctuations, a deterministic solution with the given scenario as basis and finally storing of all the solutions with a statistical analysis of the output. The algorithm is used on the two cases with two different scenario generating approaches, based on an exponential- and a continuous uniform distribution. The fleet size and mix decisions which appeared with the highest frequency were chosen, and gave a consistent estimate based on risk aversion decreasing the potential of making losses. The approaches presented in this thesis is not meant to give a correct answer on how the future will be, but help the decisions makers reduce the uncertainty connected to the strategic decision. The deterministic model give valuable information with a given scenario as input, but the model is only capable of evaluate the scenarios individually. The result found by the scenario algorithm evaluating scenarios collectively is therefore of higher value since it provide a more robust solution.

A Decision Support Methodology for Strategic Planning Under Uncertainty in Maritime Transportation

This article was first published in 2001. This is an examination of practices in aircraft evaluation and selection. It clarifies the fleet planning methodologies and defines decision-making processes that are relevant to the environment, offering insights into how selections are being made for a range of airlines and market conditions.

Paul Clark

Papers

Buying the Big Jets: Fleet Planning for Airlines : Fleet Planning for Airlines