随机需求航线网络效应下机队鲁棒优化方法

针对航线网络效应及旅客需求不确定性问题，将旅客组合优化模型加入机队规划问题，借鉴航线网络运力优化分配方法，以机型飞机数目、航段机型飞行频次、行程路线上旅客溢出人数为决策变量，以行程路线上旅客需求限制、航段飞行频次限制、特定机型机队飞行时间限制为约束条件，利用量化市场份额指数计算旅客溢出再捕获率，建立了旅客需求不确定情景下的机队鲁棒优化模型，设计了航线网络环境下的旅客需求离散情景集，用情景汇聚算法求解该模型.算例仿真结果表明，与传统机队规划模型相比较，本文模型的机队规划成本降低了167.07万元；与确定解的最小随机期望值相比，在3种情景集下，随机规划解的机队规划成本分别降低了19.88万元、21.02万元与17.55万元. 

Airline Fleet Robust Optimization Approach under Stochastic Demand with Route Network Effects

In order to solve the problem of network effects under uncertain passenger demand in route network, the itinerary-based optimization model for passenger-mix problem was incorporated into airline fleet planning problem. According to the optimization method for fleet capacity allocation, the number of aircrafts in each fleet type, the frequencies of different type of aircrafts flying on each leg, and the number of spilling passengers on each itinerary were regarded as decision variables. The limitations including the maximum passenger demand on each itinerary, available flying frequency on each flight leg, and available block time of each fleet type were considered as constraints. A robust optimization model for airline fleet planning under demand uncertainty was constructed by using the "quantitative share index" to calculate the passenger spilling recapture rate. After generating a discrete scenario set of passenger demand under the route network environment, the scenario aggregation algorithm was employed to solve the proposed model. Simulation results of an empirical example indicate that the fleet planning cost gained from this proposed model is reduced by 1 670 700 Yuan compared to the traditional model. The stochastic planning solution to fleet planning cost in 3 different scenarios decreases by 198 800 Yuan, 210 200 Yuan, and 175 500 Yuan, respectively, as opposed to the deterministic solution to the minimum stochastic expectation values.