市区行李值机服务移动站点优化方法

为提高航空运输的服务质量和竞争力，克服传统城市候机楼在服务范围有限、成本高和选址难度高等弊端，提出1种基于市区移动站点（UMS）的航空旅客行李值机服务模式。UMS基于乘客的实时位置分布差异来动态调配移动站点在城市的位置，因此需要解决UMS站点布局优化问题。综合考虑乘客到服务站点的平均路径长度和乘客最大可接受距离等2个重要指标，基于服务站点位置、不同时段的客源分布和站点的最大服务容量等限制因素对2个重要指标进行约束，建立基于路网的UMS布局优化的数学模型。为满足UMS服务模式对优化运算时效性的严格要求，提出1种混合智能优化算法，采用涟漪扩散算法（RSA）求解乘客与UMS站点多对多路径优化问题，采用自适应遗传算法（AGA）高效优化UMS位置分布。以天津城市路网的实际案例与随机生成测试案例对市区移动站点和城市候机楼2种模式的各服务时段的服务质量进行比较。结果显示:在相同站点数量的情况下，乘客到服务站点的平均路径长度比城市候机楼模式减小30.9%，超出乘客的可接受路径长度比城市候机楼模式减少43.7%；UMS位置分布优化使用混合算法（RSA-AGA），其平均计算时间为377 s，比城市候机楼模式所需的平均计算时间减少了41.2%；UMS服务模式在不同站点数量和随机生成测试案例中，各项优化目标均优于城市候机楼模式，更符合乘客的实时需求，验证了UMS运营模式的优越性。 

A Method for Improved Air Luggage Check-in Service Based on Optimized Urban Mobile Stations

To enhance the quality and competitiveness of air transport service and overcome the limitations of low service coverage, high costs, and complex site selection of traditional air terminals, this paper proposes a novel method for improved air luggage check-in service based on Urban Mobile Stations (UMS). Specifically, the proposed UMS can adapt the check-in locations to the real-time passenger positions, which is formulated as a UMS dynamic siting optimization problem over the road network. The average distance and the maximal acceptable distance from passengers to UMS are considered, incorporating the constraints on the locations of service, time-varying distribution of passengers, and the service capacity of stations. Then, a hybrid optimization algorithm satisfying the requirement of real-time computation is developed, which combines the ripple spreading algorithm (RSA) and the adaptive genetic algorithm (AGA). The RSA is used to solve the many-to-many path optimization problem of passenger and UMS stations, and the AGA is employed to optimize the UMS locations. Case studies based on the road network of Tianjin City and simulated random road networks are used for the comparison between the proposed method and the traditional method. The results show that the average distances from passengers to stations are reduced by 30.9%, the number of scenarios exceeding the maximum acceptable distance is decreased by 43.7%, and the average running time of solving the UMS optimization problem is shortened by 41.2% when using the proposed method. These facts show the advantages of the proposed UMS method, meeting the real-time passengers' demands.