Research Article  Open Access
Cooperative Game Study of Airlines Based on Flight Frequency Optimization
Abstract
By applying the game theory, the relationship between airline ticket price and optimal flight frequency is analyzed. The paper establishes the payoff matrix of the flight frequency in noncooperation scenario and flight frequency optimization model in cooperation scenario. The airline alliance profit distribution is converted into profit distribution game based on the cooperation game theory. The profit distribution game is proved to be convex, and there exists an optimal distribution strategy. The results show that joining the airline alliance can increase airline whole profit, the change of negotiated prices and cost is beneficial to profit distribution of large airlines, and the distribution result is in accordance with aviation development.
1. Introduction
With the increasing demand of air travel, not a single airline is able to achieve global seamless service. Thus, a new form of cooperation emerges in the aviation field, the airline alliance. The airline alliance [1] is a partnership formed by two or more airlines with a cooperation agreement, to establish a global flight system, a market integrated by code sharing, and so forth. The airlines in an alliance are independent from each other, while their rights and obligations are clarified in the agreement. In this paper we develop a methodology to analyze cooperative game among airline alliance market. The methodology may be used to explain airline performance and to predict the profit before and after the alliance. Game Theory is more reasonable and accurate for alliance profit distribution more than other methodologies.
The burgeoning alliance makes the distribution of profit particularly important. Literature [2–7] analyzed the development and constraints of the airline alliance and the multilateral relationships in it. Literature [8–11] discussed the general code sharing and the code sharing in the airline alliance and its influence on travelers. The airline alliance is an organization driven by market opportunity. Its fundamental purpose is to obtain economic benefits for the airlines. This is crucial not only for the stability and improvement of the alliance, but also for the participating motivation of the members. Most of the above literatures studied the profit distribution in the alliance based on assumed flight frequency. However, it is more accurate to firstly determine the flight frequency. In the paper, we analyze the relationship between the ticket price and the flight frequency under competition and cooperation circumstances and develop a profit distribution model for the airline alliance.
2. Optimal Flight Frequency in an Airline Alliance
2.1. Flight Frequency
For route , if it is a monopoly route, use plane model ; then the flight frequency for demand is [5] where is the load factor and is the number of available seats (aircraft capacity) of model .
If route is not a monopoly route, both airline A and airline B compete for it and market share of the airline A is ; then where , , and , , represent the flight frequency, aircraft capacity, and load factor of A and B, respectively; then the flight frequency for route is
2.2. Flight Frequency in Competition
In business competition, airline A and airline B usually compete on price to attract visitors by providing more discounts or increasing the number of flights. Table 1 shows the payoff matrix.
 
is the cost of flights, , , , and represent different discounts, and . , , , and , respectively, denote the flight frequency in the four cases for airline A, and according to market competition rules. While for airline , . Based on the above relations, the matrix is going to be , and . Similarly, , and it can be concluded that is the Nash equilibrium in a noncooperative game. Thus, and , respectively, are the lowest fares provided by airlines A and B, and the corresponding flight frequency is at the medium high level. 
2.3. Flight Frequency after the Airline Alliance
After joining airline alliance, the number of passengers is greatly increased due to code sharing, as well as the profit. It can be concluded that the airline alliance is one of the best ways for airlines’ benefits. The airlines of alliance are considered as one company. So the flight frequency can be simplified as follows.
The function between the profit and flight frequency is where is the airline profit; is the average fare; is per passenger cost; and and are the route running cost and the landing fee. Considering the passenger demand is a function of the flight frequency , then the derivation of flight frequency is
Introducing the demand elasticity for flight frequency , the optimal frequency of flights is
It can be seen from (6) that the optimal flight frequency is proportional to the flight fare after the alliance.
3. Profit Distribution in an Airline Alliance
3.1. The Profit Distribution Model
In an airline alliance, code sharing is a very effective marketing strategy, which decides whether consumers are willing to pay higher fares to increase their market power and space. Not only can code sharing expand the network coverage and the market presence of the alliance members, but also can strengthen the network with lower costs by reducing input of airplanes [6]. For travelers, they are provided with more choices and also enjoy a seamless transit service, because the interconnecting flights by alliance partners are strengthened by the network, where the connectivity and service quality are significantly improved.
There are three main forms of code sharing: the first is the parallel operation on the backbone routes; that is, two airlines operate their routes together and help each other sell seats; the second is the unilateral operation; that is, airline X sells the seats for airline Y, and airline Y only operates flights; the third is that airline X operates route AB and airline Y operate route BC while sharing codes with each other and selling seats of routes operated by Y [7]. In this paper, we are focusing on the first form, where the airlines operate the routes and sell seats mutually.
Airlines share codes on the same route in agreement that sets an agreement price at first. And if there are airlines, the profit distribution function for airline after code sharing is where is the agreement price and is the passenger volume on the route, is airline ’s market share on the route using the Logit model [8], is the flight operating cost, and is the number of flights after code sharing from (6). Equation (8) is the utility function of civil aviation passengers. Although travelers consider many factors when choosing airline, the service is always the first. According to the service evaluation indexes, we select the flight attendant service, the flight broadcast, the flight meals, cabin facilities, and flight entertainment as parameters.
The airline alliance is a more indepth and comprehensive cooperation based on code sharing. The primary considerations for airlines to join the alliance are the expected benefits from the alliance. The benefits mainly include having access to market and complementary resources, obtaining institutional legitimacy and capacity of new markets, and reducing risks and environmental uncertainties [9]. However, it requires plenty of cost to join the alliance, like the accession fees and annual fees when joining the international coalition. Many other costs also exist to maintain the alliance, such as management fees and coordination cost. Then the profit function of airline in code sharing period is where represents all airlines alliances, denotes the increased passenger volume due to the advantage of the alliance for airlines, and is the increased cost after alliance.
3.2. Game Model of the Profit Distribution in an Airline Alliance
The Game Theory can be applied to many questions of airline alliance. It is very effective on consultation, coordination, and achievement of strong constraint agreements. The airlines can share the benefits from the alliance through various ways of cooperation.
There are two major elements in the cooperative game: the participants and the characteristic function. The participants are the airlines involved in an alliance. If there are airlines, the set is and is a subset of , . is the corresponding characteristic function, denoting the greatest value that all members of the alliance can create, while indicates no value created if the subset is empty.
Definition 1. is an dimensional vector, satisfying the following two conditions: is called as an assignment.
Condition (10) is called the individual rational condition, indicating that for the airlines to participate in the alliance is at least better than operating by itself or it would not join the alliance. Condition (11) is called the group rational condition, which means that the sum of the profits allocated to each is the total revenue of the alliance; if less, there must be some undistributed parts of the profit, which apparently the alliance airlines will not agree with, and if greater, the total allocation exceeds the total profit of the alliance, which is impossible.
For the game of the profit distribution, , if , , and , then the game is linear; if , , , then The game also meets an additive condition, , ; then The game is a convex game.
Definition 2. For the same route, the larger the alliance, the greater the chance for the travelers to reschedule or cancel the flights, and the better the growth of travel volume is stimulated. However, , , but marketing cost can be reduced even if the coordination cost is increased; thus .
Proposition 3. Game is a convex game.
Proof. It is true, . Consider the following:
By Definition 2,
Then .
Therefore, game is a convex one. When an airline joins a larger alliance, its profit is higher than participating in a smaller one. denotes the profit assigned to airline in the alliance, where the coallocation of profit for each airline has nothing to do with its label, and the sum equals the total profit, . If airlines participate in two alliances without interference with each other, the profit distributions should also be independent from each other, and the total profit of all the airlines should equal the sum of the profit from both alliances. From Proposition 3, we know that game has a nonempty core, and the Shapley value is the core of the game and the gravity center of the collection.
can be calculated by the following formula: where is a subset of , . Equation (17) is an “impact program” under different circumstances, reflecting the power of each participated airline. A rational distribution can stimulate enthusiasm of the airlines to join the alliance.
3.3. Numerical Example
Assume that airlines A, B, and C form an alliance by cooperation of code sharing on routes. The profits of the three airlines are 61, 49, and 35 million when operating independently. The average number of daily passengers is 3500 before the alliance. The daily flight cost of A, B, and C are 6,000 yuan, 40,000 yuan, and 35,000 yuan, respectively, and the optimal flight frequency is 28 per day. is 180 passengers. is 110 passengers. is 30,000 yuan, and is 29,000 yuan.
The values of service parameters of utility function are shown in Table 2. The profit distributions under different agreement prices calculated by (7), (9), and (16) are shown in Figure 1. The absolute values of each airline’s profit allocation increase with the increase of the price , while, notably, the proportions of airline A and airline B in the total profit also tend to increase. As shown, for airline A, its proportion increases from 39.2% to 39.77% with price rising from 650 yuan to 1000 yuan, while it increases from 33.7% to 33.8% for airline B, which is less than the increase for A. However, the proportion of airline C tends to decline. It decreases from 27.1% to 26.4% when the price rises from 650 yuan to 1,000 yuan. So the higher the agreement price is, the worse it is for airline C. It becomes apparent that the airline owning more passengers and flights obtains more profits in alliance; composing alliance is especially important to such airlines.

With the same agreement price, the profit distribution of each participated airline at different alliance cost combinations can be seen in Figure 2. With the cost increasing, the profit of each airline is decreasing, and the change is significantly smaller than alliance profit, which is also an important reason why the airlines are willing to join the alliance. The proportion of airline A’s profit distribution tends to increase, such that it rises from 33.8% at (27000, 24000) to 39.5% at (80000, 75000), which is the same tendency for the proportion of C’s profit. In summary, the profit of each airline increases after joining the alliance, and the larger the airline is, the greater the benefit increases. The alliance cost does not play an important role.
4. Conclusion
It is the current and future mode of airlines to join the alliance for various ways of communication and cooperation and to seek a deeper level of development in airline industry. Flight frequencies, fares, and airline costs are always the problems that concern most airlines. In this paper, from the perspective of optimizing flight frequency, we study the flight frequency and profit distribution with both noncooperative and cooperative game before and after the alliance. We also propose a methodology to reasonably analyze alliance profit distribution using the Game Theory.
It can be concluded that the alliance can increase the overall profit for airlines; the changes in the agreement price and cost combination are more favorable to larger airlines; the results of the profit distribution accord with the actual development of airline business. With the agreement price increases, the proportion of profit distribution is increasing for larger airlines, while it has opposite effect on smaller airlines. At a fixed agreement price, the change of profit distribution is consistent with the change of the combination costs in the alliance.
In future research, we will consider the game trend and profit distribution in the network of airlines, which is closer to the airline development patterns, so as to provide more practical guidance for the progress of airline alliances.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgments
The authors would like to thank anonymous referees and the editor for their comments on the paper. Financial support from the NUAA Fundamental Research Funds (no. NS2013071) is gratefully acknowledged.
References
 G. H. Xie, “Research on profit distribution of airline alliances,” Journal of Civil Aviation Flight University, vol. 6, no. 16, pp. 30–34, 2005. View at: Google Scholar
 T. T. You, Research on Related Problems of Airline Strategic Alliance Establishment, Nanjing University of Aeronautics and Astronautics, Nanjing, China, 2008.
 B. Agusdinata and W. de Klein, “The dynamics of airline alliances,” Journal of Air Transport Management, vol. 8, no. 4, pp. 201–211, 2002. View at: Publisher Site  Google Scholar
 S. Albers, B. Koch, and C. Ruff, “Strategic alliances between airlines and airports—theoretical assessment and practical evidence,” Journal of Air Transport Management, vol. 11, no. 2, pp. 49–58, 2005. View at: Publisher Site  Google Scholar
 S. V. Gudmundsson and C. Lechner, “Multilateral airline alliances: balancing strategic constraints and opportunities,” Journal of Air Transport Management, vol. 12, no. 3, pp. 153–158, 2006. View at: Publisher Site  Google Scholar
 M. H. Lin, “Airline alliances and entry deterrence,” Transportation Research E: Logistics and Transportation Review, vol. 44, no. 4, pp. 637–652, 2008. View at: Publisher Site  Google Scholar
 J. M. LópezBonilla and L. M. LópezBonilla, “Research note: positioning strategies of global airline alliances from the consumer's perspective,” Tourism Economics, vol. 19, no. 1, pp. 203–208, 2013. View at: Publisher Site  Google Scholar
 L. Y. Kong, X. J. Li, and B. Jaing, “Method study and application of code share,” Commercial Research, no. 5, pp. 19–20, 2010. View at: Google Scholar
 H. Y. Ni, “Goodness and badness of enterprise revolution,” Chinese Aviation News, no. 3, pp. 1–2, 2012. View at: Google Scholar
 Y. Song, “Code share of airline strategy alliances,” Enterprise Revolution and Management, vol. 12, pp. 77–78, 2010. View at: Google Scholar
 T. F. Hannegan and F. P. Mulvey, “International airline alliances: an analysis of codesharing's impact on airlines and consumers,” Journal of Air Transport Management, vol. 2, no. 2, pp. 131–137, 1995. View at: Publisher Site  Google Scholar
Copyright
Copyright © 2014 Wanming Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.