Airline management simulations have come a long way over the past few years, but only recently have the techniques been established to accurately simulate passenger demand through hub and spoke networks.

The complex nature of the airline industry presents several unique and difficult situations regarding the management, distribution, and prediction of revenues and costs, and the relationship between the two. Much has been done in recent years to gain an understanding of how costs and revenues behave in such a special environment through the workings of yield management and other computer-driven algorithms. While the industry struggles with the trial and error method of dealing with the problem, academia is beginning to acquire the technology to simulate the same situation. These simulations, which are geared towards airline managers and students, are not new in theory, but have yet to benefit from the new techniques and technology available from the industry?s research are available to improve the fidelity of it?s own simulations. In this writing, we will take a close look at Airline Empires, an online airline management simulation, its current simulation of an airlines revenues, the relationship of cost to the revenues, and the inaccuracies with the current system. Much time will be spent exploring suggestions to improve the fidelity of this simulation by applying some of the knowledge gained by the industry in the area of revenue managements.

Several new generation airline management simulations have been introduced to the market as massive multi-player online games (MMPOG). Several examples on airline management MMPOG?s are AirlineSim.de, by a German company of the same name, Efzed?s Airline Online, and the new Airline Empires, created by the author of this paper. The advantage of such a situation is that a very large number of players can be used to develop a virtual economy, which is what Airline Empires is based on. While being available online attracts a large number of players into one simulation, computer capacity and performance limits the number of features and fidelity available to the MMPOG?s. This loss in fidelity can be more than compensated by the virtual economy and macro competition that is possible.

Airline Empires is the first airline simulation which allows decisions made by one player to affect the opportunities to another. Aircraft can be bought and sold between players, limited gate space requires deal-making between the players of the simulation, and most importantly, revenues are highly susceptible to the competition created by the other players. This method of managing the revenues awarded to the players is a first of its kind. Until Airline Empires, the ticket price, frequency, aircraft, or facilities of another player?s route had no bearing on the revenues generated by another. The current revenue algorithm within Airline Empires is highly sensitive to both the integral market conditions of a city pair, and the competition present in this market.

A unique demand equation was developed expressly for Airline Empires, which is used to calculate load factors. Several factors are taken into account, including city demand, frequencies offered, amenities offered, possible connecting passengers, competing flights, and competing fare?s. In a no competition situation between two cities in Airline Empires, a weighted-average of the two cities actual yearly passenger enplanements as reported by the Bureau of Transportation Statistics in table T100 is used to generate the initial demand between the cities. Distance is factored into the equation, although at a much diluted effect. The frequency offered by a player is then used to simulate the ?S-Curve? theory present in airline bookings, which offers that as daily frequencies are offered between two cities pairs increases, the number of bookings will increase in an ?S? shaped pattern until roughly 10 flights have been added, where the additional bookings drops off for every flight added (Peters).

Figure 1 - Example load factor distribution for a typical route

This is used to develop a demand equation with the y axis as demand (number of bookings) and the x-axis as the fare. The fare is then multiplied by the load factor and capacity of the aircraft to generate the total revenues from the flight. As the fare increases, the number of bookings drops off until there is nobody left. When the player changes the fare, a different x value is simply calculated, however when competition is introduced, the entire equation must be altered.