不确定容量条件下终端区流量管理关键技术研究
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摘要
容量变化是导致航班延误的重要原因，对于运输任务繁忙、空管难度较大的机场终端区而言，天气等复杂因素导致的容量不确定性给终端区流量管理带来了不小的困难，使机场终端区成为我国乃至世界空管工作的瓶颈。研究能有效应对不确定容量条件的终端区流量管理关键技术，对于保障空中交通系统运行稳定性，提高流量管理效率，减少航班延误损失，具有重要的理论意义和应用价值。
论文在对国内外相关研究成果综述的基础上，围绕不确定容量条件下终端区流量管理若干关键技术问题展开研究，主要研究内容及成果包括：
（1）为解决机场概率容量时隙资源的协同分配问题，建立了随机型协同时隙分配模型。以各种概率容量情景下的总航班延误损失期望最小和各种概率容量情景下的旅客平均延误时间最小为目标，分别确立功效性和公平性原则，在满足有效性约束的基础上，为进场航班分配时隙；根据机场容量预报的更新模式，分别建立了相应的静态、动态模型；采用非支配排序遗传算法求解模型，并借鉴传统协同时隙分配算法——RBS算法思想，分别提出了静态、动态随机型RBS算法；以国内某机场航班时刻数据为例进行仿真验证，结果表明，与经典随机型地面等待模型和所提出的随机型RBS算法相比，本文建立的随机型协同时隙分配模型在功效性和公平性方面取得了较好的优化效果，且增强了时隙分配的整体灵活性，充实了决策空间。
（2）为在机场不确定容量条件下实施有效的地面等待，建立了鲁棒型地面等待模型。分别以地面等待策略在各种情景下产生的最大总航班延误损失最小，在各种情景下产生的总航班延误损失与相应情景下最小总航班延误损失的差的绝对值的最大值最小，在各种情景下产生的总航班延误损失同相应情景下最小总航班延误损失的差与该情景下最小总航班延误损失比值的绝对值的最大值最小为目标，建立绝对、偏差、相对鲁棒优化模型；根据机场容量预报的更新模式，分别建立了相应的静态、动态模型；采用遗传算法求解模型，以国内某机场航班时刻数据为例进行仿真验证，并与等概率随机型地面等待模型进行比较，结果表明，本文建立的鲁棒型地面等待模型较好地实现了鲁棒性，为最大限度地规避风险、保障地面等待的稳定实施提供了决策依据。
（3）为解决机场不确定容量条件下的协同时隙分配问题，建立了协同时隙分配鲁棒优化模型。以总航班延误损失为功效性评价依据，以平均旅客延误时间为公平性评价依据，按照绝对、偏差、相对鲁棒性准则建立多目标优化模型，在满足有效性约束的基础上，寻求鲁棒最优的协同时隙分配策略；根据机场容量预报的更新模式，分别建立了相应的静态、动态模型；采用多目标遗传算法求解模型，以国内某机场航班时刻数据为例进行仿真验证，并与等概率随机型协同时隙分配模型进行比较，结果表明，本文建立的协同时隙分配鲁棒优化模型能够按照不同决策偏好制定具有较强鲁棒性的时隙分配策略，有效应对最劣容量情景的发生，提高了不确定容量条件下时隙分配的整体协同性和适应性。
（4）为解决机场概率容量条件下的航班进离场流量分配问题，建立了随机型终端区容流调配模型。设计航班延误风险控制策略，以一定延误风险水平下的总航班延误损失最小为目标，在优化利用容量的基础上，为每个航班安排合适的进离场时段，在控制延误风险的同时兼顾流量与容量的匹配；根据终端区类型，分别建立了相应的单机场终端区模型和多机场终端区模型；采用捕食搜索算法求解模型，以国内某终端区航班时刻数据为例进行仿真验证，并与经典终端区容流调配模型进行比较，结果表明，相同延误风险水平下，本文建立的机型终端区容流调配模型取得了较好的优化效果，有效减少了航班延误损失。
（5）为解决机场不确定容量条件下的航班进离场流量分配问题，建立了终端区容流调配鲁棒优化模型。分别以容流调配策略在一定鲁棒性水平下的总延误损失最小，在各情景下的规划外延误损失或多余延误损失的均值与标准差之和最小，在各情景下的规划外延误损失或多余延误损失的最大值最小为目标，建立遗憾、差异、偏好鲁棒优化模型；根据终端区类型，分别建立了相应的单机场终端区模型和多机场终端区模型；采用捕食搜索算法求解模型，以国内某终端区航班时刻数据为例进行仿真验证，并与经典终端区容流调配模型进行比较，结果表明，相同鲁棒性水平下，本文建立的终端区容流调配遗憾鲁棒模型取得了较好的优化效果，差异和偏好鲁棒模型实现了相应的鲁棒性，有效地减少了不同情景下进离场航班延误扰动。
论文研究丰富了终端区流量管理的理论方法，为不确定条件下的流量管理研究提供了新思路，也为流量管理决策提供了参考。
The change of capacity is a significant factor that may cause flight delay. Especially for airportterminal area with large number of flights and heavy workload of air traffic management. Theuncertainty of airport capacity resulted from weather brings much difficulty in terminal air traffic flowmanagement. Airport terminal area has been a bottleneck for domestic or world-wide air trafficmanagement. Studying the key technologies of terminal air traffic flow management which adapt touncertain capacity conditions is with important theoretical and practical significance for ensuring thestability of air traffic system, improving the efficiency of air traffic flow management and reducingflight delay.
On the basis of literature review for domestic and foreign related research, the paper studied severalkey problems of terminal air traffic flow management with uncertain capacity conditions. The maincontents and achievements are listed below.
First, to solve collaborative slot allocation with airport probability capacity, a set of stochasticcollaborative slot allocation models was proposed. With the aim to minimize the expected total flightdelay cost and the expected average delay time of passengers, the principles of efficiency and equitywere established. On the basis of the constraints of effectiveness, slots were allocated to arrival flights.According to the form of airport capacity being updated, both static model and dynamic model forwere proposed. Non-dominated sorting genetic algorithm was applied in solving the models, andstatic stochastic RBS and dynamic stochastic RBS were proposed based on tradditional RBS. Set theflight schedul of one of domestic airports as an example, a numerical test was performed. Test resultsshow that, compared with typical stochastic models and stochastic RBS algorithms, the modelsproposed win them and could efficiently, equitably and effectively allocate slot with airportprobability capacity. Test results also show that, the models improve the flexibility of slot allocationand enrich the decision space.
Second, to effectively perform ground delay program with airport uncertain capacity, a set of robustground delay program models was proposed. With the aim to respectively minimize the total flightdelay cost in each capacity scenario, the maximum deviation between total flight delay cost and theoptimal total flight delay cost in each capacity scenario as well as the ratio, the principles ofrobustness were established. According to the form of airport capacity being updated, both staticmodel and dynamic model for were proposed. Genetic algorithm was applied in solving the models.Set the flight schedul of one of domestic airports as an example, a numerical test was performed. Test results show that, compared with stochastic models under equal probability scenarios, the modelsproposed are better than them in achieving the goals of robustness and provide available reference fordecision making.
Third, to solve collaborative slot allocation with airport uncertain capacity, a set of robustcollaborative slot allocation models was proposed. Set the total flight delay cost and the average delaytime of passengers as the basis of the principles of efficiency and equity respectively, absolute robust,deviation robust, and relative robust models were established. On the basis of the constraints ofeffectiveness, robust optimal strategies for slot allocation were searched for. According to the form ofairport capacity being updated, both static model and dynamic model were proposed. Non-dominatedsorting genetic algorithm was applied in solving the models. Set the flight schedul of one of domesticairports as an example, a numerical test was performed. Test results show that, compared withstochastic collaborative models under equal probability scenarios, the models proposed couldefficiently, equitably and effectively allocate slot and improve the stability of slot allocation withuncertain capacity.
Forth, to solve the arrival and departure flow allocation with airport probability capacity, a set ofstochastic flow allocation models for both single airport terminal area and multi-airport terminal areawas proposed. The strategy for maintaining the risk of flight delay was introduced in. With the aim tominimize the total flight delay cost under certain risk of flight delay, the models utilized the capacityand optimized the arrival and departure flow. A new intelligent algorithm named predatory searchalgorithm was applied in solving the models. Set the flight schedul of one of domestic terminals as anexample, a numerical test was performed. Test results show that, compared with typical models, themodels proposed gain a better effect than them and could effectively reduce flight delay cost with thesame delay risk.
Fifth, to solve the arrival and departure flow allocation with airport uncertain capacity, a set of flowallocation robust optimization models for both single airport terminal area and multi-airport terminalarea was proposed. With the aim to respectively minimize the total delay cost with certain robustfactor, average value plus standard deviation of delay cost in each capacity scenario and the maximumloss of delay cost, regreat robust, differential robust and preference robust models were proposed.Predatory search algorithm was applied in solving the models. Set the flight schedul of one ofdomestic terminals as an example, a numerical test was performed. Test results show that, comparedwith typical models, regreat robust models proposed gain a better effect than them with the samerobust factor. Test results also show that, differential robust and preference robust models proposedcould effectively be with robustness on the basis of different decision preferences and reduce thedisturbances of flight delay in each capacity scenario.
The paper enriched the theory and method of terminal air traffic flow management, and provided anew way to study air traffic flow management with uncertain capacity. Besides, the paper provided areference for air traffic flow management decision making in real world.
论文在对国内外相关研究成果综述的基础上，围绕不确定容量条件下终端区流量管理若干关键技术问题展开研究，主要研究内容及成果包括：
（1）为解决机场概率容量时隙资源的协同分配问题，建立了随机型协同时隙分配模型。以各种概率容量情景下的总航班延误损失期望最小和各种概率容量情景下的旅客平均延误时间最小为目标，分别确立功效性和公平性原则，在满足有效性约束的基础上，为进场航班分配时隙；根据机场容量预报的更新模式，分别建立了相应的静态、动态模型；采用非支配排序遗传算法求解模型，并借鉴传统协同时隙分配算法——RBS算法思想，分别提出了静态、动态随机型RBS算法；以国内某机场航班时刻数据为例进行仿真验证，结果表明，与经典随机型地面等待模型和所提出的随机型RBS算法相比，本文建立的随机型协同时隙分配模型在功效性和公平性方面取得了较好的优化效果，且增强了时隙分配的整体灵活性，充实了决策空间。
（2）为在机场不确定容量条件下实施有效的地面等待，建立了鲁棒型地面等待模型。分别以地面等待策略在各种情景下产生的最大总航班延误损失最小，在各种情景下产生的总航班延误损失与相应情景下最小总航班延误损失的差的绝对值的最大值最小，在各种情景下产生的总航班延误损失同相应情景下最小总航班延误损失的差与该情景下最小总航班延误损失比值的绝对值的最大值最小为目标，建立绝对、偏差、相对鲁棒优化模型；根据机场容量预报的更新模式，分别建立了相应的静态、动态模型；采用遗传算法求解模型，以国内某机场航班时刻数据为例进行仿真验证，并与等概率随机型地面等待模型进行比较，结果表明，本文建立的鲁棒型地面等待模型较好地实现了鲁棒性，为最大限度地规避风险、保障地面等待的稳定实施提供了决策依据。
（3）为解决机场不确定容量条件下的协同时隙分配问题，建立了协同时隙分配鲁棒优化模型。以总航班延误损失为功效性评价依据，以平均旅客延误时间为公平性评价依据，按照绝对、偏差、相对鲁棒性准则建立多目标优化模型，在满足有效性约束的基础上，寻求鲁棒最优的协同时隙分配策略；根据机场容量预报的更新模式，分别建立了相应的静态、动态模型；采用多目标遗传算法求解模型，以国内某机场航班时刻数据为例进行仿真验证，并与等概率随机型协同时隙分配模型进行比较，结果表明，本文建立的协同时隙分配鲁棒优化模型能够按照不同决策偏好制定具有较强鲁棒性的时隙分配策略，有效应对最劣容量情景的发生，提高了不确定容量条件下时隙分配的整体协同性和适应性。
（4）为解决机场概率容量条件下的航班进离场流量分配问题，建立了随机型终端区容流调配模型。设计航班延误风险控制策略，以一定延误风险水平下的总航班延误损失最小为目标，在优化利用容量的基础上，为每个航班安排合适的进离场时段，在控制延误风险的同时兼顾流量与容量的匹配；根据终端区类型，分别建立了相应的单机场终端区模型和多机场终端区模型；采用捕食搜索算法求解模型，以国内某终端区航班时刻数据为例进行仿真验证，并与经典终端区容流调配模型进行比较，结果表明，相同延误风险水平下，本文建立的机型终端区容流调配模型取得了较好的优化效果，有效减少了航班延误损失。
（5）为解决机场不确定容量条件下的航班进离场流量分配问题，建立了终端区容流调配鲁棒优化模型。分别以容流调配策略在一定鲁棒性水平下的总延误损失最小，在各情景下的规划外延误损失或多余延误损失的均值与标准差之和最小，在各情景下的规划外延误损失或多余延误损失的最大值最小为目标，建立遗憾、差异、偏好鲁棒优化模型；根据终端区类型，分别建立了相应的单机场终端区模型和多机场终端区模型；采用捕食搜索算法求解模型，以国内某终端区航班时刻数据为例进行仿真验证，并与经典终端区容流调配模型进行比较，结果表明，相同鲁棒性水平下，本文建立的终端区容流调配遗憾鲁棒模型取得了较好的优化效果，差异和偏好鲁棒模型实现了相应的鲁棒性，有效地减少了不同情景下进离场航班延误扰动。
论文研究丰富了终端区流量管理的理论方法，为不确定条件下的流量管理研究提供了新思路，也为流量管理决策提供了参考。
The change of capacity is a significant factor that may cause flight delay. Especially for airportterminal area with large number of flights and heavy workload of air traffic management. Theuncertainty of airport capacity resulted from weather brings much difficulty in terminal air traffic flowmanagement. Airport terminal area has been a bottleneck for domestic or world-wide air trafficmanagement. Studying the key technologies of terminal air traffic flow management which adapt touncertain capacity conditions is with important theoretical and practical significance for ensuring thestability of air traffic system, improving the efficiency of air traffic flow management and reducingflight delay.
On the basis of literature review for domestic and foreign related research, the paper studied severalkey problems of terminal air traffic flow management with uncertain capacity conditions. The maincontents and achievements are listed below.
First, to solve collaborative slot allocation with airport probability capacity, a set of stochasticcollaborative slot allocation models was proposed. With the aim to minimize the expected total flightdelay cost and the expected average delay time of passengers, the principles of efficiency and equitywere established. On the basis of the constraints of effectiveness, slots were allocated to arrival flights.According to the form of airport capacity being updated, both static model and dynamic model forwere proposed. Non-dominated sorting genetic algorithm was applied in solving the models, andstatic stochastic RBS and dynamic stochastic RBS were proposed based on tradditional RBS. Set theflight schedul of one of domestic airports as an example, a numerical test was performed. Test resultsshow that, compared with typical stochastic models and stochastic RBS algorithms, the modelsproposed win them and could efficiently, equitably and effectively allocate slot with airportprobability capacity. Test results also show that, the models improve the flexibility of slot allocationand enrich the decision space.
Second, to effectively perform ground delay program with airport uncertain capacity, a set of robustground delay program models was proposed. With the aim to respectively minimize the total flightdelay cost in each capacity scenario, the maximum deviation between total flight delay cost and theoptimal total flight delay cost in each capacity scenario as well as the ratio, the principles ofrobustness were established. According to the form of airport capacity being updated, both staticmodel and dynamic model for were proposed. Genetic algorithm was applied in solving the models.Set the flight schedul of one of domestic airports as an example, a numerical test was performed. Test results show that, compared with stochastic models under equal probability scenarios, the modelsproposed are better than them in achieving the goals of robustness and provide available reference fordecision making.
Third, to solve collaborative slot allocation with airport uncertain capacity, a set of robustcollaborative slot allocation models was proposed. Set the total flight delay cost and the average delaytime of passengers as the basis of the principles of efficiency and equity respectively, absolute robust,deviation robust, and relative robust models were established. On the basis of the constraints ofeffectiveness, robust optimal strategies for slot allocation were searched for. According to the form ofairport capacity being updated, both static model and dynamic model were proposed. Non-dominatedsorting genetic algorithm was applied in solving the models. Set the flight schedul of one of domesticairports as an example, a numerical test was performed. Test results show that, compared withstochastic collaborative models under equal probability scenarios, the models proposed couldefficiently, equitably and effectively allocate slot and improve the stability of slot allocation withuncertain capacity.
Forth, to solve the arrival and departure flow allocation with airport probability capacity, a set ofstochastic flow allocation models for both single airport terminal area and multi-airport terminal areawas proposed. The strategy for maintaining the risk of flight delay was introduced in. With the aim tominimize the total flight delay cost under certain risk of flight delay, the models utilized the capacityand optimized the arrival and departure flow. A new intelligent algorithm named predatory searchalgorithm was applied in solving the models. Set the flight schedul of one of domestic terminals as anexample, a numerical test was performed. Test results show that, compared with typical models, themodels proposed gain a better effect than them and could effectively reduce flight delay cost with thesame delay risk.
Fifth, to solve the arrival and departure flow allocation with airport uncertain capacity, a set of flowallocation robust optimization models for both single airport terminal area and multi-airport terminalarea was proposed. With the aim to respectively minimize the total delay cost with certain robustfactor, average value plus standard deviation of delay cost in each capacity scenario and the maximumloss of delay cost, regreat robust, differential robust and preference robust models were proposed.Predatory search algorithm was applied in solving the models. Set the flight schedul of one ofdomestic terminals as an example, a numerical test was performed. Test results show that, comparedwith typical models, regreat robust models proposed gain a better effect than them with the samerobust factor. Test results also show that, differential robust and preference robust models proposedcould effectively be with robustness on the basis of different decision preferences and reduce thedisturbances of flight delay in each capacity scenario.
The paper enriched the theory and method of terminal air traffic flow management, and provided anew way to study air traffic flow management with uncertain capacity. Besides, the paper provided areference for air traffic flow management decision making in real world.
引文
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