International Airline Alliance Network Design with Uncertainty
Abstract
:1. Introduction
2. Literature Review
3. Airline Alliance Network
3.1. Problem Formulation
 The cooperation between alliance partners should meet the limitations of traffic rights.
 The roundtrip passenger flow of international routes usually differs greatly and is asymmetric.
 Usually, the domestic network of an airline is relatively complete before the airline joins an alliance, and the regional hubs can be given. However, airlines need to choose their international gateways from the set of regional hubs through optimization.
 One or more international gateways coexist with each area for each airline, and all gateways are interconnected across international areas.
 All international gateways are interconnected across international areas. Due to the economy of scale in which passengers converge at gateway airports, a discount factor $\alpha $ is incorporated into the cost between international gateways.
 According to the actual transportation situation, international transportation usually does not exceed two transits, so airports other than the international gateways are regarded as “spokes” of international routes. Multiallocation connections between “spokes” and international gateways are adopted; $\chi $ is the discount factor from “spokes” to international gateways, and $\delta $ is the discount factor from international gateways to “spokes”. Generally, $0\le \alpha <\chi ,\delta \le 1$.
 The regional hubs within each area are given, and airlines choose their international gateways from this subset of regional hubs.
 Interarea journeys are limited to three legs, that is, if both the origin and destination nodes are “spokes”, traveling across international areas will necessarily involve a threeleg journey. For example, to travel from i5 to i7 would involve one leg from i5 to K1, a second leg from K1 to K2, and, finally, a third leg from K2 to i7. On the other hand, interarea journeys contain at least one leg, and this happens when both the originating and destination nodes are the international gateways.
 Interarea traffic must all be transported from the originating node to the destination node.
 The roundtrip passenger demand of international routes is usually asymmetric; ${W}_{ij}\ne {W}_{ji}$.
3.2. Alliance Route Network Model
3.3. Optimal Solution
Algorithm 1 An Iterative Optimization Algorithm for the Alliance Route Network 

4. Case Study
4.1. Data Settings
4.2. Computational Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Node  Airport  Node  Airport  Node  Airport 

1  PEK*\Beijing  7  CKG\Chongqing  13  DTW*\Detroit 
2  CAN\Guangzhou  8  XIY*\Xi’an  14  LAX*\Los Angeles 
3  PVG*\Shanghai  9  WUH*\Wuhan  15  MSP*\Minneapolis 
4  CTU\Chengdu  10  NKG\Nanjing  16  SFO\San Francisco 
5  SZX\Shenzhen  11  ATL*\Atlanta  17  SEA\Seattle 
6  KMG*\Kunming  12  JFK*\New York  18  ORD\Chicago 
Short Haul  Medium Haul  Long Haul  

CNY per ASK  0.3724  0.2894  0.2941 
Scenario Number  CASKSH Deviation (%)  CASKMH Deviation (%)  CASKLH Deviation (%) 

1  0  0  0 
2  20  20  20 
3  −20  −20  −20 
4  20  20  −20 
5  20  −20  20 
6  −20  20  20 
7  20  −20  −20 
8  −20  20  −20 
9  −20  −20  20 
$\mathit{\alpha}=0.2$  $\mathit{\alpha}=0.4$  $\mathit{\alpha}=0.6$  $\mathit{\alpha}=0.8$  

Intl. GL  Trans. Costs  Intl. GL  Trans. Costs  Intl. GL  Trans. Costs  Intl. GL  Trans. Costs  
s1  3, 12, 14  223,129,610  1, 12, 14  365,549,030  1, 12, 14  506,512,065  1, 13, 14  647,009,456 
s2  3, 12, 14  267,755,532  1, 12, 14  438,658,836  1, 12, 14  607,814,479  1, 13, 14  776,411,347 
s3  3, 12, 14  178,503,688  1, 12, 14  292,439,224  1, 12, 14  405,209,652  1, 13, 14  517,607,565 
s4  3, 12, 14  208,191,272  1, 12, 14  325,888,407  1, 12, 14  438,658,836  1, 12, 14  551,429,264 
s5  1, 12, 14  241,491,590  1, 12, 14  410,647,234  1, 12, 14  579,766,838  1, 14, 15  743,667,894 
s6  3, 13, 14  260,392,325  1, 13, 14  429,436,112  1, 13, 14  596,487,252  1, 13, 14  763,538,391 
s7  3, 12, 14  183,698,164  3, 12, 14  297,876,805  1, 12, 14  410,647,234  1, 12, 14  523,417,662 
s8  3, 13, 14  201,692,264  1, 13, 14  318,068,686  1, 13, 14  429,436,112  1, 13, 14  540,803,538 
s9  1, 12, 14  236,054,009  1, 12, 14  405,209,652  1, 14, 15  572,475,719  1, 14, 15  736,129,225 
RM  3, 12, 14  1, 12, 14  1, 12, 14  1, 12, 14 
p = 2  p = 3  p = 4  p = 5  

$\alpha =0.2$  3, 14  3, 12, 14  1, 3, 12, 14  1, 3, 12, 13, 14 
$\alpha =0.4$  1, 14  1, 12, 14  1, 3, 12, 14  1, 3, 12, 13, 14 
$\alpha =0.6$  1, 14  1, 12, 14  1, 3, 12, 14  1, 3, 12, 14, 15 
$\alpha =0.8$  1, 14  1, 12, 14  1, 3, 12, 14  1, 3, 12, 14, 15 
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Yang, W.; Shao, J.; Jiang, Y.; Xu, Z.; Tsourdos, A. International Airline Alliance Network Design with Uncertainty. Appl. Sci. 2021, 11, 3065. https://doi.org/10.3390/app11073065
Yang W, Shao J, Jiang Y, Xu Z, Tsourdos A. International Airline Alliance Network Design with Uncertainty. Applied Sciences. 2021; 11(7):3065. https://doi.org/10.3390/app11073065
Chicago/Turabian StyleYang, Wendong, Jiajia Shao, Yun Jiang, Zhengjia Xu, and Antonios Tsourdos. 2021. "International Airline Alliance Network Design with Uncertainty" Applied Sciences 11, no. 7: 3065. https://doi.org/10.3390/app11073065