Analyzing the Airlines Competition by Players Clustering in the Game Theory Approach

Document Type : Original Article

Authors

1 Ph.D. Student, Payame Noor University, Iran.

2 Associate Professor, Industrial Engineering Department, Engineering Faculty, Payame Noor University, Iran.

3 Professor, School of Industrial, University of Tehran, Tehran, Iran.

4 Professor, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.

10.22034/tri.2021.102463

Abstract

The large number of heterogeneous players makes difficult to determine the equilibrium point in the game theory, so firstly the players are clustered and then the game theory is used in two stages. First, the clusters compete to determine the point of equilibrium. Then the players within each cluster compete to determine the share and the payoff for each player. In this paper, the results of the game theory in the competition between 47 Airlines at the Imam Khomeini Airport are evaluated by statistical tests. The payoff functions in this model are the cost of the stand and the share of the jet way stand for each airline. The results show that the players clustering can be useful when the number of the players is high. ANOVA and the T student test in paired observations was used For the validation of the proposed algorithm.The result of P-Value = 1 were obtained by ANOVA with α = 0.05 in Minitab software. The share of jet way stand for airlines in the number of clusters 11, 12, 13, 15 and 47 (without clustering mode) did not differ significantly. Also the T student test in paired observations was performed to compare the non-clustering (k = 47) with different clustering states in the number of 11, 12, 13 and 15 clusters. Respectively P-Value results were obtained 0.930. 0.947, 0.944, 0.964. These results shown that there is no significant difference in the contribution of Airlines from the jet stand without clustering and clustering. The results of this research show how to using clustering in game theory approach on a real world case. Also considering airlines as a member of transportation modes can be useful.

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References

-اصغرپور. م.، (1382)، "تصمیم‌گیری گروهی و نظریه بازی‌ها با نگرش تحقیق در عملیات"، انتشارات سمت دانشگاه تهران، تهران، ایران. ص.288­.
 
-تمنایی، م. راستی برزکی, م.، (1397)، "رویکرد نظریه بازی برای مدل سازی رقابت در بازار حمل و نقل جاده­ای و ریلی کالا، پژوهشنامه حمل و نقل.
-مومنی، م، خوشه بندی داده ها، (1395)، ناشر مولف، تهران، ایران.
 
-نویدی. ح، کتابچی. س، مسی بیدگلی. م، (1390)، "مدخلی بر نظریه بازی­ها"، انتشارات دانشگاه شاهد، تهران، ایران. ص.3- 4.
 
 
-Castaing , j. Mukherjee, I., (2016), “Reducing airport gate blockage in passenger aviation: Models and analysis. Computers & Operations Research”.
 
-Dae Ko, Y., (2016), “An airline's management strategies in a competitive air transport market”, Journal of Air Transport Management 50 , pp.53-61.
 
-Dell’Orco, M.,  Marinelli, M., Giovanna Altieri, M., (2017),  “Solving the gate assignment problem through the Fuzzy Bee Colony Optimization”, Transportation Research Part C.
-Evans, A. Schäfer, A.,  (2014), "Simulating airline operational  responses to airport capacity constraints" Transport Policy, Vol.  34,   pp. 5–13.
 
-Grauberger, W. Kimms, A., (2014), "Decision Support Computing approximate Nash equilibria in general network revenue management games", European Journal Operational Research,  Vol.   237, 
pp.  1008–1020.
 
-Guépet, J . cuna-Agostb, b,R.A. Brianta, O.Gayon J.P., (2015), “Exact and heuristic approaches to the airport stand allocation problem”, European Journal of Operational Research 246, pp.597–608.
 
-Halkidi, M., Batistakis, Y., and Vazirgiannis, M., (2002), “Clustering validity methods: Part I. ACM SIGMOD Record”, 31(2).
 
-Han, S., Li, X., Liu, Z., & Guan, X., (2016), “Distributed hierarchical game-based algorithm for downlink power allocation in OFDMA femtocell networks. Computer Networks, 94, pp.176-188.
 
-Knabe, F, Schultz, M., (2016), “A new way to indicate airport airside performance from an economic perspective”, Transportation Research Procedia 14. pp. 3771 – 3780.
 
-Leonard, T. Bekker, J., (2011), " Comparing airport apron layout designs using computer simulation and the cross-entropy method", Thesis presented in partial ful_lment of the requirements for the degree of Master of Science in the Faculty of Engineering at Stellenbosch University.
 
-­Lim Sengpoh, (­2015­), "The Competitive Pricing Behaviour of Low Cost Airlines in the Perspective of Sun Tzu Art of Ware". Procedia - Social and Behavioral Sciences”, Vol.  172. pp. 741– 748.
 
- Romesburg, H. C., (1984), “Cluster analysis for researchers”, Lulu Press.
 
-Sharma, S., (1996), “Applied multivariate techniques, New York: John Wiley & Sons”.
-Souza, M. A. D. S., Weigang, L. I., & Garcia, R. C., (2018)., “Stable two-sided matching of slot allocation in airport collaborative decision making by top trading cycles mechanism”, Chinese Journal of Aeronautics, 31(3), pp.34-545.
 
 
 
 
 
 
 
 
 
-Tang, C, Wang, W., (2013), “Airport gate assignments for airline-specific gates. Airport Management”, Elsevier Journal of Air Transport Management 30 (2013) pp.10-16.
 
-Yalong, W., Xi, L., Heli, Z., & Ke, W., (2016), "Resource allocation scheme based on game theory in heterogeneous networks", The Journal of China Universities of Posts and Telecommunications, 23(3), pp.57-88.
 
-YU, P. L., (1973a.), “A Class of Solution for Group Decision Prolem”. Management Science, Vol. 19, No. 8, pp. 936-946.
 
-Zhao,X, Atkins, D, Hu, M,  Zhang, W.,(2017),  “Revenue management under joint pricing and capacity allocation competition”, European Journal of Operational Research, Vol. 257 . pp.  957–970.
 
-Zitoa, P. Salvoa, G. Fran, L., (2011), "Modelling Airlines Competition on Fares and Frequencies of Service by Bi-level Optimization", Procedia Social and Behavioral Sciences. Vol.   20, 
pp.  1080–1089.

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