基于减少旅客延误的航班中转行程优化

为减少中转行程中产生的旅客延误,在考虑中转过程中后序航班旅客延误的情况下,以中转旅客与后序航班旅客的总延误最小为目标建立优化模型;在满足航班运行限制的约束条件下,应用种马遗传算法对模型进行求解;使用北京首都国际机场的客流数据进行实例验证,并对不同旅客最短衔接时间的优化结果进行比较.研究结果表明:与经典遗传算法和增强精英保留遗传算法相比,种马遗传算法具有更快的收敛速度;在旅客最短衔接时间为120 min的条件下,优化后中转行程中产生的旅客延误减少了29.9％;当旅客最短衔接时间越小时产生的旅客延误越少,且模型优化效果较为稳定,优化百分比为25％ ～30％.

The Optimization of Flight Transfer Journey Based on Reducing Passenger Delays

In order to reduce the passenger delay in the transfer journey, the optimization model is set up to minimize the total delay between the transfer passengers and the subsequent flight passengers when considering the delay of the subsequent flight passengers in the transit process. The stud genetic algorithm is applied to solve the model under the constraint of flight operation. An example is given to verify the passenger flow data of Beijing Capital International Airport, and the optimization results of the minimum connecting time for different passengers are compared. The results show that compared with classical genetic algorithm and strengthen elitist genetic algorithm, stud genetic algorithm has a faster convergence rate. With the minimum connecting time of 120 minutes, the passenger delay during the optimized transfer journey was reduced by 29.9%. When the passenger connection time is shorter, the passenger delay will be less, and the optimization effect of the model is relatively stable, with the optimization percentage between 25% and 30%.