Abstract: This paper studies the hub location problem (HLP) of air cargo network under Aviation Alliance based on the random number and location of hub points and the limitation of capacity. Firstly, the probability function of route alliance selection is introduced to determine the probability of self operating or outsourcing transportation of routes in different segments. Secondly, according to the location center rule, the hub location model is established with the objective of minimizing the network total cost. And then the improved immune chaos genetic algorithm is used to solve the model. Finally, the case of SF Airlines is analyzed. The results show that: 1)compared with the immune chaos genetic algorithm and the immune genetic algorithm and CPLEX, the improved algorithm has strong convergence and calculation speed, and the calculation results are not much different from the accurate algorithm; 2)when the number of hub points is uncertain, the location of the hub points is mostly concentrated in the eastern cities; 3)airlines alliance can greatly reduce operating costs. Airlines company should consider joining the alliance to reduce their own costs and improve their profits.

Key words: air freight, transport hub point location model, improved immune chaotic genetic algorithms, air alliance selection probability function

摘要： 本文研究航空联盟下航空货运网络的枢纽点选址问题(HLP),基于枢纽点的数量及位置随机与容量的限制。首先引入航线联盟选择概率函数,确定不同航段上的航线自营运输或外包运输的概率;其次根据选址中心法则,以网络总成本最小化为目标,建立枢纽点选址模型;再次采用改进的免疫混沌遗传算法求解模型;最后,以顺丰航空公司案例进行实例分析。结果表明:1)本文改进的算法较免疫混沌遗传算法并与免疫遗传算法及CPLEX结果对比,发现本文设计的算法有较强的收敛性和计算速度,且计算结果与CPLEX求解器求解结果相差不大;2)枢纽点数量不确定时,枢纽点的位置多集中在东部城市;3)航空公司选择联盟环境可以大大降低运营成本,航空公司为提高自身利润,应考虑加入联盟,从而降低自身成本。

关键词: 航空货运, 枢纽点选址模型, 改进免疫混沌遗传算法, 航线联盟选择概率函数