机场终端区协同流量管理关键技术研究
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摘要
航空运输业的持续快速发展,势必引起空中交通流量迅速增长,空中交通需求的急剧增加与容量供给相对缩小之间的矛盾将日益突出,空域管理者和使用者之间的矛盾也将愈演愈烈,运行流量管理协同决策机制是构建我国和谐空管的迫切需要。机场终端区作为我国空管的瓶颈,研究相应的协同流量管理关键技术的重要意义不言而喻。
论文全面分析了国内外协同流量管理的发展现状、发展趋势及其最新研究成果;深入剖析了机场终端区协同流量管理的相关理论基础、经典方法以及主要研究成果;系统论述了机场终端区协同流量管理的主要研究内容、关键问题和未来的研究方向。
研究了机场终端区交通供需平衡问题,提出了协同容流调配理念,建立了基于多元受限的单机场和多机场终端区协同容流调配模型。从大多数机场进离场相关的实际情况出发,基于进离场容量相互转化和系统容量动态变化,建立协同容流调配优化模型。算例仿真结果表明,模型可以在协同最优化容量与流量的同时兼顾航空公司利益,增加容流调配的实效性、灵活性和自主性,使之更趋合理。
研究了协同地面延误程序(CDM GDP)时隙分配问题,提出了基于有效性、功效性和公平性(3E)的CDM GDP时隙分配优化方法,建立了相应的量化评价指标。根据CDM原理,按照我国社会的资源分配原则,通过考虑技术有效性、总体功效性和合理公平性,建立了基于3E的CDM GDP时隙分配单目标和多目标优化模型,模型可以在充分利用受限资源的前提下,实现时隙分配的最优性和延误分摊的均衡性。通过考虑机场容量的动态性和进离场航班的相关性,拓展深化研究了CDM GDP时隙分配问题,提出了协同进离场地面延误程序(CDM ADGDP)和多机场协同地面延误程序(CDM MAGDP),分别建立了基于3E的进离场容量与时隙协同配置模型,实现了容量与时隙的合理、有效配置。
研究了协同地面延误程序(CDM GDP)时隙交换问题,提出了基于MAS协调的CDM GDP时隙动态交易机制与策略。借鉴SCS时隙交易机制和市场机制,提出了有条件式时隙拍卖机制,采用单边付款的形式实现交易过程,并以效用表示航空公司的自主决策,建立了基于BDI的AOC Agent协调交易推理策略,提高了CDM GDP时隙交换的自主性、协同性和适用性。针对“一对一”简单交换和“多对多”复杂交换问题,分别给出了相应的MAS协调交易模型。
研究了航班调度问题(ASP),提出了协同航班调度策略,建立了基于优先权和基于多准则的协同航班调度模型。基于优先权的协同航班调度模型,集成了空管、机场和航空公司等因素,通过引入航班优先级转移航班之间的延误,实现了航空公司的内部自主决策;基于多准则的协同航班调度模型,综合了空管、机场和航空公司等因素,通过采用安全、效率、公平和负荷等多准则,在确保安全的条件下,最大限度地降低了延误,并使延误在航空公司之间均摊,同时又兼顾了管制工作负荷。
研究了协同流量管理系统,概要提出了适合我国的协同流量管理系统的体系结构和总体构架;结合本论文研究的关键技术,应用MAS方法,初步设计了终端区协同流量管理系统,为下一步的研发提供借鉴与支持。
最后,总结了本论文的主要研究成果,并展望以后的研究方向。
本论文的研究不仅完善了机场终端区流量管理理论与方法,而且对决策者在实际中的应用也具有一定的指导意义。
With increasingly development of air transport industry, it is bound to cause rapid growth of air traffic flow; it will highlight the contradiction between the sharp increase of demand and the relatively decrease of supply, as well as the contradiction between airspace managers and its users. So running CDM mechanisms in ATFM is a necessity for building harmonious ATM in China. Since airport terminal area is the bottleneck of ATM, the importance to research key technologies in CDM is self-evident.
In this dissertation, the status quo, development trend and the latest research results of collaborative flow management are summarized comprehensively; the relevant theoretical foundation, classical methods and main research results are analyzed deeply; the main research content, key issues and future trends of the theory in airport terminal area are dissertated systematically.
The dissertation pursues a systemic research on the balance problems between supply and demand, it puts forward the concept of collaborative allocating capability and flow, and presents optimization models based on multi-restrict constraints for single airport terminal area and multi-airport terminal area. Since the arrivals and departures are two interdependent processes in most of airport, it builds collaborative optimization models of allocating capability-flow based on arrival-departure capability trade-offs and dynamic characteristics. The results show that these proposed models can provide optimal allocating schemes of capacity and flow with airline’s preferences, increase effectiveness and autonomy.
The slot allocation problems are studied at great length in CDM GDP, optimization models are proposed based on the principle of Effectiveness-Efficiency-Equity (3E) in single airport and multi-airport, and some indexes are also introduced to evaluate equity among airlines. According to the resource allocation principles in Chinese society, a single-objective optimization model and multi-objective optimization model are built based on 3E principle under CDM mechanism. These models are able to make best use of limited resources, minimize the total delay cost and balance it among airlines. Taking dynamic capacity and the relativity of arrival-departure into account, an Arrival-Departure GDP (ADGDP) and Multi-Airport GDP (MAGDP) in CDM are presented, the corresponding optimization model are built to collaboratively allocate airport capability and slot resources.
The slot exchange problems are also studied deeply in CDM GDP, a dynamic slot trading mechanism and strategy are presented based on MAS coordination. Learning from SCS and a market mechanism, a slot trading mechanism with conditional auction is proposed. It uses unilateral payment form to implement transactions, expresses airlines’autonomous decision-making by utility, and builds a coordination trading reasoning strategy for AOC Agent based on BDI. Simulation results show that it can raise the autonomy, coordination and applicability of slot exchange in CDM GDP. Corresponding MAS coordination trading models are built for both simple exchange and complex exchange problems.
The dissertation pursues an in-depth research on aircraft scheduling problem (ASP), puts forward a collaborative aircraft scheduling strategy, and proposes a priority-based optimization model and a multi-objection optimization model with the interests of ATC, airports and airlines. The priority-based optimization model is capable of transferring one flight’s delay to another by priority so as to make intramural decision for airlines; the multi-objection optimization model is able to ensure safety, minimize the total delay cost and balance it among airlines, and minimize ATC workload.
The dissertation also summarily proposes the architecture and framework of collaborative flow management system in China, and preliminarily designs a system based on MAS with proposed key problems in airport terminal area, these can provide reference and support for next R & D.
In the end, the dissertation summarizes the main research and outlooks for further study.
In a word, the dissertation’s researches not only consummate flow management theory and methods in airport terminal area, but also have some guiding significance to policymakers in practical application.
论文全面分析了国内外协同流量管理的发展现状、发展趋势及其最新研究成果;深入剖析了机场终端区协同流量管理的相关理论基础、经典方法以及主要研究成果;系统论述了机场终端区协同流量管理的主要研究内容、关键问题和未来的研究方向。
研究了机场终端区交通供需平衡问题,提出了协同容流调配理念,建立了基于多元受限的单机场和多机场终端区协同容流调配模型。从大多数机场进离场相关的实际情况出发,基于进离场容量相互转化和系统容量动态变化,建立协同容流调配优化模型。算例仿真结果表明,模型可以在协同最优化容量与流量的同时兼顾航空公司利益,增加容流调配的实效性、灵活性和自主性,使之更趋合理。
研究了协同地面延误程序(CDM GDP)时隙分配问题,提出了基于有效性、功效性和公平性(3E)的CDM GDP时隙分配优化方法,建立了相应的量化评价指标。根据CDM原理,按照我国社会的资源分配原则,通过考虑技术有效性、总体功效性和合理公平性,建立了基于3E的CDM GDP时隙分配单目标和多目标优化模型,模型可以在充分利用受限资源的前提下,实现时隙分配的最优性和延误分摊的均衡性。通过考虑机场容量的动态性和进离场航班的相关性,拓展深化研究了CDM GDP时隙分配问题,提出了协同进离场地面延误程序(CDM ADGDP)和多机场协同地面延误程序(CDM MAGDP),分别建立了基于3E的进离场容量与时隙协同配置模型,实现了容量与时隙的合理、有效配置。
研究了协同地面延误程序(CDM GDP)时隙交换问题,提出了基于MAS协调的CDM GDP时隙动态交易机制与策略。借鉴SCS时隙交易机制和市场机制,提出了有条件式时隙拍卖机制,采用单边付款的形式实现交易过程,并以效用表示航空公司的自主决策,建立了基于BDI的AOC Agent协调交易推理策略,提高了CDM GDP时隙交换的自主性、协同性和适用性。针对“一对一”简单交换和“多对多”复杂交换问题,分别给出了相应的MAS协调交易模型。
研究了航班调度问题(ASP),提出了协同航班调度策略,建立了基于优先权和基于多准则的协同航班调度模型。基于优先权的协同航班调度模型,集成了空管、机场和航空公司等因素,通过引入航班优先级转移航班之间的延误,实现了航空公司的内部自主决策;基于多准则的协同航班调度模型,综合了空管、机场和航空公司等因素,通过采用安全、效率、公平和负荷等多准则,在确保安全的条件下,最大限度地降低了延误,并使延误在航空公司之间均摊,同时又兼顾了管制工作负荷。
研究了协同流量管理系统,概要提出了适合我国的协同流量管理系统的体系结构和总体构架;结合本论文研究的关键技术,应用MAS方法,初步设计了终端区协同流量管理系统,为下一步的研发提供借鉴与支持。
最后,总结了本论文的主要研究成果,并展望以后的研究方向。
本论文的研究不仅完善了机场终端区流量管理理论与方法,而且对决策者在实际中的应用也具有一定的指导意义。
With increasingly development of air transport industry, it is bound to cause rapid growth of air traffic flow; it will highlight the contradiction between the sharp increase of demand and the relatively decrease of supply, as well as the contradiction between airspace managers and its users. So running CDM mechanisms in ATFM is a necessity for building harmonious ATM in China. Since airport terminal area is the bottleneck of ATM, the importance to research key technologies in CDM is self-evident.
In this dissertation, the status quo, development trend and the latest research results of collaborative flow management are summarized comprehensively; the relevant theoretical foundation, classical methods and main research results are analyzed deeply; the main research content, key issues and future trends of the theory in airport terminal area are dissertated systematically.
The dissertation pursues a systemic research on the balance problems between supply and demand, it puts forward the concept of collaborative allocating capability and flow, and presents optimization models based on multi-restrict constraints for single airport terminal area and multi-airport terminal area. Since the arrivals and departures are two interdependent processes in most of airport, it builds collaborative optimization models of allocating capability-flow based on arrival-departure capability trade-offs and dynamic characteristics. The results show that these proposed models can provide optimal allocating schemes of capacity and flow with airline’s preferences, increase effectiveness and autonomy.
The slot allocation problems are studied at great length in CDM GDP, optimization models are proposed based on the principle of Effectiveness-Efficiency-Equity (3E) in single airport and multi-airport, and some indexes are also introduced to evaluate equity among airlines. According to the resource allocation principles in Chinese society, a single-objective optimization model and multi-objective optimization model are built based on 3E principle under CDM mechanism. These models are able to make best use of limited resources, minimize the total delay cost and balance it among airlines. Taking dynamic capacity and the relativity of arrival-departure into account, an Arrival-Departure GDP (ADGDP) and Multi-Airport GDP (MAGDP) in CDM are presented, the corresponding optimization model are built to collaboratively allocate airport capability and slot resources.
The slot exchange problems are also studied deeply in CDM GDP, a dynamic slot trading mechanism and strategy are presented based on MAS coordination. Learning from SCS and a market mechanism, a slot trading mechanism with conditional auction is proposed. It uses unilateral payment form to implement transactions, expresses airlines’autonomous decision-making by utility, and builds a coordination trading reasoning strategy for AOC Agent based on BDI. Simulation results show that it can raise the autonomy, coordination and applicability of slot exchange in CDM GDP. Corresponding MAS coordination trading models are built for both simple exchange and complex exchange problems.
The dissertation pursues an in-depth research on aircraft scheduling problem (ASP), puts forward a collaborative aircraft scheduling strategy, and proposes a priority-based optimization model and a multi-objection optimization model with the interests of ATC, airports and airlines. The priority-based optimization model is capable of transferring one flight’s delay to another by priority so as to make intramural decision for airlines; the multi-objection optimization model is able to ensure safety, minimize the total delay cost and balance it among airlines, and minimize ATC workload.
The dissertation also summarily proposes the architecture and framework of collaborative flow management system in China, and preliminarily designs a system based on MAS with proposed key problems in airport terminal area, these can provide reference and support for next R & D.
In the end, the dissertation summarizes the main research and outlooks for further study.
In a word, the dissertation’s researches not only consummate flow management theory and methods in airport terminal area, but also have some guiding significance to policymakers in practical application.
引文
[1]中国民用航空发展第十一个五年规划[R].中国民用航空局, 2006.
[2]张兆宁,王莉莉.空中交通流量管理理论与方法[M].北京:科学出版社, 2009.
[3]高海军,王健,陈龙等.空中交通流量管理理综述[J].控制工程, 2003, 10(6):484-487.
[4] Ball M.O., Hoffman R., Inniss T, et al. Collaborative decision making for air traffic management: a preliminary assessment [R]. NEXTOR T.R. RR-99-3, 1998.
[5] Ball M.O., Hoffman R., Chen C.Y, et al. Collaborative decision making in air traffic management: current and future research directions [R]. NEXTOR T.R. 2000-3, 2000.
[6]吕小平.空中交通管理协同决策[J].空中交通管理, 2005, 4:4-5.
[7]李泉.基于CDM的ATFM策略分析及资源分配机制研究[D].南京:南京航空航天大学硕士论文, 2006.
[8] Wambsganns M. Collaborative decision making through dynamic information transfer [J]. Air Traffic Control Quarterly, 1997, 4(2):109-125.
[9] Hall C. Information flows and a dynamic collaborative decision-making architecture for increasing the efficiency of terminal area operations [D], PhD Dissertation, Operations Research Center, MIT, 1999.
[10] Chang K., Howard K., Oiesen R., et al. Enhancements to the FAA ground delay program under collaborative decision making [J]. Interfaces, 2001, 31(1):57-76.
[11] Stirnman M., Rothmann V., Graham R.,et al. C-ATM high-level operational concept [R]. The C-ATM consortium, 2005.
[12]杨元元.中国发展新一代民用航空运输系统的愿景[J].中国民用航空, 2007, 8:16-18.
[13]吕小平.中国民航新一代空中交通管理系统发展总体框架[J].中国民用航空, 2007, 8:24-26.
[14] Odoni A.R. The flow management problem in air traffic control [C]. Odoni A.R, Bianco L., Szego G., Flow control of congested neworks, Springer-verlag, Berlin, 1987:269-288.
[15] Terrab, M. Ground Holding Strategies for Air Traffic Control [D]. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, Mass,1990.
[16] Richetta O., Odoni A.R.. Solving optimally the static ground-holding policy problem in air traffic control [J]. Transportation Science, 1993, 27(3):228-238.
[17] Vranas P.B., Bertisimas D.J., Odoni A.R. The multi-airport ground holding problem in air traffic control [J]. Operations Research, 1994, 42(2): 249-261.
[18] Vranas P.B., Bertisimas D.J., Odoni A.R.. Dynamic ground-holding policies for a network of airports [J]. Transportation Science, 1994, 28(4):275-261.
[19]胡明华,徐肖豪,陈爱民.空中交通流量管理中的多元受限地面等待策略问题研究[J].航空学报, 1998, 19(1):78-82.
[20]胡明华,钱爱东,苏兰根.多机场地面等待问题模型研究[J].南京航空航天大学学报, 2000, 32(5):586-590.
[21]胡明华,李丹阳,李顺才.空中交通地面等待问题的网络流规划模型[J].东南大学学报(自然科学版), 2000, 30(3):104-108.
[22]王来军,史忠科.需求随机型单机场地面等待问题的优化建模研究[J].计算机工程与应用, 2004, 40(34):34-36.
[23]王莉莉,张兆宁.一种新的单机场地面等待问题优化算法[J].飞行力学, 2005, 23(2):85-88.
[24]王飞,徐肖豪,张静.基于人工鱼群算法的单机场地面等待优化策略[J].南京航空航天大学学报, 2009, 41(1):116-120.
[25] Andreussi A., Bianco L., Ricciardelli S.. A simulation model for aircraft sequencing in the near terminal area [J]. European Journal of Operational Research, 1981, 8(4):345-354.
[26] Beasley J.E, Krishnamoolthy M., Sharaiha Y.M., et a1. Scheduling aircraft landings-the static case [J]. Transport Science, 2000, 34(2):180-97.
[27] Beasley J.E, Sonander J., Havelock P.. Scheduling aircraft landings at London Heathrow using a population heuristic [J]. Journal of the Operational Research Society, 2001, 52(5):483-493.
[28] Fahle T, Feldmann R, G?tz S, et al. The aircraft sequencing problem [C]. Klein R. etal, Computer Science in Perspective, New York: Springer-Verlag Berlin Heidelberg, 2003:152-166.
[29] Capri S., Ignaccolo M., Genetic algorithms for solving the aircraft-sequencing problem: the introduction of departures into the dynamic model [J]. Journal of Air Transport Management, 2004, 10(5):345-351.
[30]荀海波,徐肖豪,陈绪华.机场终端区着陆次序的排序规划算法[J].南京航空航天大学学报, 1999, 31(2):178-184.
[31]胡明华,李丹阳,韩松臣.被动空中交通流量管理中的动态排序算法[J].南京航空航天大学学报, 2000, 32(1):85-90.
[32]徐肖豪,黄宝军.终端区飞机排序的模糊综合评判方法研究[J].航空学报, 2001, 22(3):259-261.
[33]余江,刘晓明,蒲云.飞机着陆调度问题的MPS优化算法研究[J].系统工程理论与实践, 2004, 24(3):119-122.
[34]杨秋辉,游志胜,冯子亮等.一种改进的基于遗传算法的多跑道到达飞机调度[J].四川大学学报(工程科学版), 2006, 38(2):141-145.
[35]王飞,徐肖豪,张静.终端区飞机排序的混合人工鱼群算法[J].交通运输工程学报, 2008, 8(3):68-72.
[36] Bertsimas D, Patterson S.S. The air traffic flow management problem with enroute apacities [J]. Operations Research, 1998, 46(3): 406-422.
[37] Dimitris Bertsimas. The traffic management rerouting problem in air traffic control: A dynamic network flow approach [J]. Transportation Science, 2000, 34(3): 239.
[38]田勇,宋柯,顾英豪.空中交通流量管理中的改航策略研究[J].数学的实践与认识, 2008, 38(10):70-76.
[39] Milner J.M. Dynamic slot allocation with airline participation [D]. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, Mass, 1995.
[40] Hoffman R.L.. Integer programming modes for ground-holding in air traffic flow management [D]. Doctor of philosophy, University of Maryland, 1997.
[41] Gilbo E P. Optimizing airport capacity utilization in air traffic flow management subject to constraints at arrival and departure fixes [J]. IEEE Transactions on Control Systems Technology, 1997, 5(5):490-503.
[42]余江,王大海,浦云.终端区起飞着陆的协同优化[J].系统工程学报, 2003, 18(5):462-465.
[43]马正平,崔德光,谢玉兰.机场终端区流量分配及优化调度[J].清华大学学报(自然科学版), 2003, 43(7):876-879.
[44]陈欣,陆迅,朱金福.枢纽机场空侧容量利用和流量分配优化模型[J].南京航空航天大学学报, 2007, 39(5):680-684.
[45] Paolo D.O, Guglielmo L. A dynamic programming approach for the airport capacity allocation problem [J]. IMA Journal of Management Mathematics, 2003, 14(3):235-249.
[46] Gilbo E P, Howard K W. Collaborative optimization of airport arrival and departure traffic flow management strategies for CDM [C]. 3rd USA/Europe Air Traffic Management R&D Seminar, Napoli, 2000:1-11.
[47]张洪海,胡明华,陈世林.机场进离场流量协同分配策略[J].南京航空航天大学学报, 2008, 40(5):641-645.
[48]张洪海,胡明华,陈世林.机场终端区容量利用和流量分配协同优化策略[J].西南交通大学学报, 2009, 44(1):128-134.
[49] Hoffman R., Hall W., Ball M., et al. Collaborative decision making in air traffic flowmanagement [Z]. Manuscript in preparation. 1999.
[50] Thomas W.M. Vossen. Fair allocation methods in air traffic management [D]. PhD thesis, University of Maryland, Maryland, 2002.
[51] Ball M.O. Collaborative mechanisms for managing airport capacity reductions in the U.S [R] NEXTOR technical report. University of Maryland, 2008.
[52] Ball M.O., Hoffman R., Vossen T.. Optimization and mediated bartering models for ground delay programs [Z]. 2002.
[53] Vossen T., Ball M.. Optimization and mediated bartering models for ground delay programs [J]. Naval Research Logistics, 2006, 53(1): 75-90.
[54] Sankararaman R.. Impact assessment of dynamic slot exchange in air traffic management [D]. Master thesis, University of Maryland, Maryland, 2004.
[55]周茜,张学军,柳重堪. CDM GDP程序中混合使用跑道时隙分配问题研究[J].空中交通管制, 2005, 5:23-26.
[56] Thomas W.M. Vossen, Michael O. Ball. Slot trading opportunities in collaborative ground delay programs [J]. Transportation science, 2006, 40 (1):29-43.
[57]周茜,张学军,柳重堪.时隙分配算法在CDM GDP程序中的应用[J].北京航空航天大学学报, 2006, 32(9):1043-1045.
[58]董云龙.空中交通流量管理资源公平分配算法研究[D].南京:南京航空航天大学硕士论文,2007.
[59]陈世林,胡明华.一种新的基于贪婪法的CDM时隙分配最优化算法[J].系统工程理论与实践, 2008, 28(10):144-149.
[60]张荣,胡明华,张洪海.协同式地面等待程序中时隙公平分配研究[J].交通与计算机, 2008, 26(5):62-65.
[61] Soomer M.J., Franx G.J.. Scheduling aircraft landings using airlines’preferences [J]. European Journal of Operational Research, 2008, 190(1):277-291.
[62] Zhang Honghai, Hu Minghua, Wang Yanjun, Gu Yinghao. Priority-based collaborative aircraft scheduling [J]. Proceedings of the Eighth International Conference of Chinese Logistics and Transportation Professionals, the American Society of Civil Engineers, 2008, 2:1087-1092.
[63]贾利民,刘刚,秦勇.基于智能Agent的动态协作任务求解[M].北京:科学出版社, 2007:106-135.
[64] WEAVER KM.深入了解协同[Z]. http://www.weaver.com.cn/weaverKM/CC_define.asp
[65] Chambliss A.G... Collaborative routing concept exploration transition report [R]. Mitre technicalreport, MTR 98W0000104, 1998.
[66] Shantz A.. Weather avoidance planning and collaborative routing decisions [C]. 2nd USA/Europe air traffic management R&D seminar, Orlando, 1998:1-16.
[67] Collaborative routing sub-group. A framework for collaborative routing [R]. Internal CDM report, 2002, http://cdm.fly.faa.gov/.
[68] Wolfe, S.R., Enomoto, F.Y., Jarvis, P.A., et al. Comparing route selection strategies in collaborative traffic flow management [C]. Proceedings of the 2007 IEEE/WIC/ACM international Conference on intelligent Agent Technology, IEEE Computer Society, Washington, D.C., 2007:59-62.
[69] Lockwood S., Atkins S., Dorighi N. Surface management system simulations in NASA’S future flight central [C]. AIAA Guidance, Navigation, and Control Conference, Monterey, CA, 2002:1-8.
[70] Spencer A, Smith P, Billings C, et al. Decision support tools to assist in airport surface management [C]. IEEE Systems, Manm, and Cybernetics Conference, IEEE, 2003:1606-1611.
[71]陈世林,胡明华,张洪海. SMS中基于冲突探测的滑行道轨迹预测算法研究[J].四川大学学报(自然科学版), 2008, 45(6):1357-1361.
[72]胡明华,钱爱东,苏兰根.基于地面等待策略的航班时刻规划方法[J].航空学报, 2001, 23(3):262-264.
[73]马正平,崔德光.机场航班延误优化模型[J].清华大学学报(自然科学版), 2004, 44(4):474-477.
[74]李宝民,史忠科.空中交通流量优化管理[J].飞行力学, 1998, 16(4):7-13.
[75] Gilbo E.P. Airport capacity: representation, estimation, optimization [J]. IEEE Transactions on Control Systems Technology, 1993, 1(3):144-154.
[76]王小平,曹立明.遗传算法理论、应用与软件实现[M].西安:西安交通大学出版社, 2002.
[77]中国民用航空总局. CCAR-93TM-R:中国民用航空空中交通管理规则[S].民航总局令第86号, 1999.
[78]徐肖豪,李雄.航班地面等待模型中的延误成本分析与仿真[J].南京航空航天大学学报, 2006, 38(1):115-120.
[79] Delahaye Daniel, Sofiane Oussedik, Puechmorel stephane. Airspace congestion smoothing by multi-objective genetic algorithm[C]. ACM Symposium on Applied Computing, 2005: 907-912.
[80]谷峰,陈华平,卢冰原.基于均匀设计的多目标遗传算法在柔性工作车间调度中的应用[J].系统工程理论方法应用, 2006, 15(6):548-551.
[81] Robert L. Hoffman. Defining. Measuring, and Implementing Equity [R]. Equitable Allocation of Limited Resources Final Report, 2003.
[82]陈燕.公平与效率[M].中国社会科学出版社,北京, 2007.
[83]郭东强.基于有效性、功效性和公平性均衡的企业信息资源分配模型[J].厦门大学学报(自然科学版), 2001, 40(6):1196-1201.
[84]叶剑英.智能系统非经典数学方法[M].武汉:华中科技大学出版社, 2004.
[85]陶世群,浦保兴.基于遗传算法的多级目标非平衡指派问题求解[J].系统工程理论与实践, 2004, 24(8):80-85.
[86]崔逊学.多目标进化算法及其应用[M].国防工业出版社,北京, 2006.
[87]李玉榕,项国波.一种基于多目标遗传算法的非线性控制器[J].计算机仿真, 2004, 21(5):61-63.
[88]游进军,纪昌明,付湘.基于遗传算法的多目标问题求解方法[J].水利学报, 2003, 7:64-69.
[89]易伟华,陈学广,刘振元.基于MAS协调的资源分配决策[J].系统工程理论与实践, 2006, 3:11-16.
[90] Barber K.S., Han D.C., Liu T.H.. Coordinating distributed decision making using reusable interaction specification [J]. Lecture Notes in Computer Science, 2000, 1881:1-15.
[91]张维明,姚莉.智能协作信息技术[M].北京:电子工业出版社, 2002.
[92]易伟华,夏海光,陈学广.基于BDI和知识水平的分布式协调推理[J].系统工程, 2004, 22(7):93-98.
[93]胡山立,石纯一. Agent的意图模型[J].软件学报, 2000, 11(10):965-970.
[94] Rao A.S, Georgeff M.P. BDI agents from theory to practice [A]. Technical Note 56, AAII[C], 1995.
[95]杨军利,向小军.基于最小成本的飞机着陆规划算法[J].中国民航学院学报, 2006, 24(2):34-36.
[96]周明,孙树栋.遗传算法原理及应用[M].北京:国防工业出版社, 2002.
[97]薛劲松,宋宏. CIMS的总体设计[M].北京:机械工业出版社, 1997.
[98]陈炜炜,耿睿,崔德光.进近区域到达航班排序和调度的优化[J].清华大学学报(自然科学版), 2006, 46(1):157-160.
[99]黎新华,张兆宁.基于Agent的空中交通流量管理系统结构研究[J].交通运输工程与信息学报, 2007, 5(1):56-61.
[100]马正平,崔德光.空中交通战略和战术级流量管理模型[J].清华大学学报, 2003, 43(7):903-907.
[101]罗喜伶.空中交通流量管理系统中关键技术研究[D].北京:北京航空航天大学博士论文, 2002.
[102]赵嶷飞.空中交通流量管理系统研究[D].北京:北京航空航天大学博士论文, 2003.
[103]蔡自兴.人工智能及其应用[M].北京:清华大学出版社, 2004.
[104]史忠植.智能主体及其应用[M].北京:科学出版社, 2000.
[105]张洪海,胡明华,陈世林.基于Agent的终端区流量协同管理研究[C].中国交通研究与探索:第七届全国交通运输领域青年学术会议论文集, 2007:528~532.
[106]王飞.基于Multi-Agent的空中交通协同流量管理[J].广西师范大学学报:自然科学版,2008, 26(10):125-128.
[2]张兆宁,王莉莉.空中交通流量管理理论与方法[M].北京:科学出版社, 2009.
[3]高海军,王健,陈龙等.空中交通流量管理理综述[J].控制工程, 2003, 10(6):484-487.
[4] Ball M.O., Hoffman R., Inniss T, et al. Collaborative decision making for air traffic management: a preliminary assessment [R]. NEXTOR T.R. RR-99-3, 1998.
[5] Ball M.O., Hoffman R., Chen C.Y, et al. Collaborative decision making in air traffic management: current and future research directions [R]. NEXTOR T.R. 2000-3, 2000.
[6]吕小平.空中交通管理协同决策[J].空中交通管理, 2005, 4:4-5.
[7]李泉.基于CDM的ATFM策略分析及资源分配机制研究[D].南京:南京航空航天大学硕士论文, 2006.
[8] Wambsganns M. Collaborative decision making through dynamic information transfer [J]. Air Traffic Control Quarterly, 1997, 4(2):109-125.
[9] Hall C. Information flows and a dynamic collaborative decision-making architecture for increasing the efficiency of terminal area operations [D], PhD Dissertation, Operations Research Center, MIT, 1999.
[10] Chang K., Howard K., Oiesen R., et al. Enhancements to the FAA ground delay program under collaborative decision making [J]. Interfaces, 2001, 31(1):57-76.
[11] Stirnman M., Rothmann V., Graham R.,et al. C-ATM high-level operational concept [R]. The C-ATM consortium, 2005.
[12]杨元元.中国发展新一代民用航空运输系统的愿景[J].中国民用航空, 2007, 8:16-18.
[13]吕小平.中国民航新一代空中交通管理系统发展总体框架[J].中国民用航空, 2007, 8:24-26.
[14] Odoni A.R. The flow management problem in air traffic control [C]. Odoni A.R, Bianco L., Szego G., Flow control of congested neworks, Springer-verlag, Berlin, 1987:269-288.
[15] Terrab, M. Ground Holding Strategies for Air Traffic Control [D]. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, Mass,1990.
[16] Richetta O., Odoni A.R.. Solving optimally the static ground-holding policy problem in air traffic control [J]. Transportation Science, 1993, 27(3):228-238.
[17] Vranas P.B., Bertisimas D.J., Odoni A.R. The multi-airport ground holding problem in air traffic control [J]. Operations Research, 1994, 42(2): 249-261.
[18] Vranas P.B., Bertisimas D.J., Odoni A.R.. Dynamic ground-holding policies for a network of airports [J]. Transportation Science, 1994, 28(4):275-261.
[19]胡明华,徐肖豪,陈爱民.空中交通流量管理中的多元受限地面等待策略问题研究[J].航空学报, 1998, 19(1):78-82.
[20]胡明华,钱爱东,苏兰根.多机场地面等待问题模型研究[J].南京航空航天大学学报, 2000, 32(5):586-590.
[21]胡明华,李丹阳,李顺才.空中交通地面等待问题的网络流规划模型[J].东南大学学报(自然科学版), 2000, 30(3):104-108.
[22]王来军,史忠科.需求随机型单机场地面等待问题的优化建模研究[J].计算机工程与应用, 2004, 40(34):34-36.
[23]王莉莉,张兆宁.一种新的单机场地面等待问题优化算法[J].飞行力学, 2005, 23(2):85-88.
[24]王飞,徐肖豪,张静.基于人工鱼群算法的单机场地面等待优化策略[J].南京航空航天大学学报, 2009, 41(1):116-120.
[25] Andreussi A., Bianco L., Ricciardelli S.. A simulation model for aircraft sequencing in the near terminal area [J]. European Journal of Operational Research, 1981, 8(4):345-354.
[26] Beasley J.E, Krishnamoolthy M., Sharaiha Y.M., et a1. Scheduling aircraft landings-the static case [J]. Transport Science, 2000, 34(2):180-97.
[27] Beasley J.E, Sonander J., Havelock P.. Scheduling aircraft landings at London Heathrow using a population heuristic [J]. Journal of the Operational Research Society, 2001, 52(5):483-493.
[28] Fahle T, Feldmann R, G?tz S, et al. The aircraft sequencing problem [C]. Klein R. etal, Computer Science in Perspective, New York: Springer-Verlag Berlin Heidelberg, 2003:152-166.
[29] Capri S., Ignaccolo M., Genetic algorithms for solving the aircraft-sequencing problem: the introduction of departures into the dynamic model [J]. Journal of Air Transport Management, 2004, 10(5):345-351.
[30]荀海波,徐肖豪,陈绪华.机场终端区着陆次序的排序规划算法[J].南京航空航天大学学报, 1999, 31(2):178-184.
[31]胡明华,李丹阳,韩松臣.被动空中交通流量管理中的动态排序算法[J].南京航空航天大学学报, 2000, 32(1):85-90.
[32]徐肖豪,黄宝军.终端区飞机排序的模糊综合评判方法研究[J].航空学报, 2001, 22(3):259-261.
[33]余江,刘晓明,蒲云.飞机着陆调度问题的MPS优化算法研究[J].系统工程理论与实践, 2004, 24(3):119-122.
[34]杨秋辉,游志胜,冯子亮等.一种改进的基于遗传算法的多跑道到达飞机调度[J].四川大学学报(工程科学版), 2006, 38(2):141-145.
[35]王飞,徐肖豪,张静.终端区飞机排序的混合人工鱼群算法[J].交通运输工程学报, 2008, 8(3):68-72.
[36] Bertsimas D, Patterson S.S. The air traffic flow management problem with enroute apacities [J]. Operations Research, 1998, 46(3): 406-422.
[37] Dimitris Bertsimas. The traffic management rerouting problem in air traffic control: A dynamic network flow approach [J]. Transportation Science, 2000, 34(3): 239.
[38]田勇,宋柯,顾英豪.空中交通流量管理中的改航策略研究[J].数学的实践与认识, 2008, 38(10):70-76.
[39] Milner J.M. Dynamic slot allocation with airline participation [D]. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, Mass, 1995.
[40] Hoffman R.L.. Integer programming modes for ground-holding in air traffic flow management [D]. Doctor of philosophy, University of Maryland, 1997.
[41] Gilbo E P. Optimizing airport capacity utilization in air traffic flow management subject to constraints at arrival and departure fixes [J]. IEEE Transactions on Control Systems Technology, 1997, 5(5):490-503.
[42]余江,王大海,浦云.终端区起飞着陆的协同优化[J].系统工程学报, 2003, 18(5):462-465.
[43]马正平,崔德光,谢玉兰.机场终端区流量分配及优化调度[J].清华大学学报(自然科学版), 2003, 43(7):876-879.
[44]陈欣,陆迅,朱金福.枢纽机场空侧容量利用和流量分配优化模型[J].南京航空航天大学学报, 2007, 39(5):680-684.
[45] Paolo D.O, Guglielmo L. A dynamic programming approach for the airport capacity allocation problem [J]. IMA Journal of Management Mathematics, 2003, 14(3):235-249.
[46] Gilbo E P, Howard K W. Collaborative optimization of airport arrival and departure traffic flow management strategies for CDM [C]. 3rd USA/Europe Air Traffic Management R&D Seminar, Napoli, 2000:1-11.
[47]张洪海,胡明华,陈世林.机场进离场流量协同分配策略[J].南京航空航天大学学报, 2008, 40(5):641-645.
[48]张洪海,胡明华,陈世林.机场终端区容量利用和流量分配协同优化策略[J].西南交通大学学报, 2009, 44(1):128-134.
[49] Hoffman R., Hall W., Ball M., et al. Collaborative decision making in air traffic flowmanagement [Z]. Manuscript in preparation. 1999.
[50] Thomas W.M. Vossen. Fair allocation methods in air traffic management [D]. PhD thesis, University of Maryland, Maryland, 2002.
[51] Ball M.O. Collaborative mechanisms for managing airport capacity reductions in the U.S [R] NEXTOR technical report. University of Maryland, 2008.
[52] Ball M.O., Hoffman R., Vossen T.. Optimization and mediated bartering models for ground delay programs [Z]. 2002.
[53] Vossen T., Ball M.. Optimization and mediated bartering models for ground delay programs [J]. Naval Research Logistics, 2006, 53(1): 75-90.
[54] Sankararaman R.. Impact assessment of dynamic slot exchange in air traffic management [D]. Master thesis, University of Maryland, Maryland, 2004.
[55]周茜,张学军,柳重堪. CDM GDP程序中混合使用跑道时隙分配问题研究[J].空中交通管制, 2005, 5:23-26.
[56] Thomas W.M. Vossen, Michael O. Ball. Slot trading opportunities in collaborative ground delay programs [J]. Transportation science, 2006, 40 (1):29-43.
[57]周茜,张学军,柳重堪.时隙分配算法在CDM GDP程序中的应用[J].北京航空航天大学学报, 2006, 32(9):1043-1045.
[58]董云龙.空中交通流量管理资源公平分配算法研究[D].南京:南京航空航天大学硕士论文,2007.
[59]陈世林,胡明华.一种新的基于贪婪法的CDM时隙分配最优化算法[J].系统工程理论与实践, 2008, 28(10):144-149.
[60]张荣,胡明华,张洪海.协同式地面等待程序中时隙公平分配研究[J].交通与计算机, 2008, 26(5):62-65.
[61] Soomer M.J., Franx G.J.. Scheduling aircraft landings using airlines’preferences [J]. European Journal of Operational Research, 2008, 190(1):277-291.
[62] Zhang Honghai, Hu Minghua, Wang Yanjun, Gu Yinghao. Priority-based collaborative aircraft scheduling [J]. Proceedings of the Eighth International Conference of Chinese Logistics and Transportation Professionals, the American Society of Civil Engineers, 2008, 2:1087-1092.
[63]贾利民,刘刚,秦勇.基于智能Agent的动态协作任务求解[M].北京:科学出版社, 2007:106-135.
[64] WEAVER KM.深入了解协同[Z]. http://www.weaver.com.cn/weaverKM/CC_define.asp
[65] Chambliss A.G... Collaborative routing concept exploration transition report [R]. Mitre technicalreport, MTR 98W0000104, 1998.
[66] Shantz A.. Weather avoidance planning and collaborative routing decisions [C]. 2nd USA/Europe air traffic management R&D seminar, Orlando, 1998:1-16.
[67] Collaborative routing sub-group. A framework for collaborative routing [R]. Internal CDM report, 2002, http://cdm.fly.faa.gov/.
[68] Wolfe, S.R., Enomoto, F.Y., Jarvis, P.A., et al. Comparing route selection strategies in collaborative traffic flow management [C]. Proceedings of the 2007 IEEE/WIC/ACM international Conference on intelligent Agent Technology, IEEE Computer Society, Washington, D.C., 2007:59-62.
[69] Lockwood S., Atkins S., Dorighi N. Surface management system simulations in NASA’S future flight central [C]. AIAA Guidance, Navigation, and Control Conference, Monterey, CA, 2002:1-8.
[70] Spencer A, Smith P, Billings C, et al. Decision support tools to assist in airport surface management [C]. IEEE Systems, Manm, and Cybernetics Conference, IEEE, 2003:1606-1611.
[71]陈世林,胡明华,张洪海. SMS中基于冲突探测的滑行道轨迹预测算法研究[J].四川大学学报(自然科学版), 2008, 45(6):1357-1361.
[72]胡明华,钱爱东,苏兰根.基于地面等待策略的航班时刻规划方法[J].航空学报, 2001, 23(3):262-264.
[73]马正平,崔德光.机场航班延误优化模型[J].清华大学学报(自然科学版), 2004, 44(4):474-477.
[74]李宝民,史忠科.空中交通流量优化管理[J].飞行力学, 1998, 16(4):7-13.
[75] Gilbo E.P. Airport capacity: representation, estimation, optimization [J]. IEEE Transactions on Control Systems Technology, 1993, 1(3):144-154.
[76]王小平,曹立明.遗传算法理论、应用与软件实现[M].西安:西安交通大学出版社, 2002.
[77]中国民用航空总局. CCAR-93TM-R:中国民用航空空中交通管理规则[S].民航总局令第86号, 1999.
[78]徐肖豪,李雄.航班地面等待模型中的延误成本分析与仿真[J].南京航空航天大学学报, 2006, 38(1):115-120.
[79] Delahaye Daniel, Sofiane Oussedik, Puechmorel stephane. Airspace congestion smoothing by multi-objective genetic algorithm[C]. ACM Symposium on Applied Computing, 2005: 907-912.
[80]谷峰,陈华平,卢冰原.基于均匀设计的多目标遗传算法在柔性工作车间调度中的应用[J].系统工程理论方法应用, 2006, 15(6):548-551.
[81] Robert L. Hoffman. Defining. Measuring, and Implementing Equity [R]. Equitable Allocation of Limited Resources Final Report, 2003.
[82]陈燕.公平与效率[M].中国社会科学出版社,北京, 2007.
[83]郭东强.基于有效性、功效性和公平性均衡的企业信息资源分配模型[J].厦门大学学报(自然科学版), 2001, 40(6):1196-1201.
[84]叶剑英.智能系统非经典数学方法[M].武汉:华中科技大学出版社, 2004.
[85]陶世群,浦保兴.基于遗传算法的多级目标非平衡指派问题求解[J].系统工程理论与实践, 2004, 24(8):80-85.
[86]崔逊学.多目标进化算法及其应用[M].国防工业出版社,北京, 2006.
[87]李玉榕,项国波.一种基于多目标遗传算法的非线性控制器[J].计算机仿真, 2004, 21(5):61-63.
[88]游进军,纪昌明,付湘.基于遗传算法的多目标问题求解方法[J].水利学报, 2003, 7:64-69.
[89]易伟华,陈学广,刘振元.基于MAS协调的资源分配决策[J].系统工程理论与实践, 2006, 3:11-16.
[90] Barber K.S., Han D.C., Liu T.H.. Coordinating distributed decision making using reusable interaction specification [J]. Lecture Notes in Computer Science, 2000, 1881:1-15.
[91]张维明,姚莉.智能协作信息技术[M].北京:电子工业出版社, 2002.
[92]易伟华,夏海光,陈学广.基于BDI和知识水平的分布式协调推理[J].系统工程, 2004, 22(7):93-98.
[93]胡山立,石纯一. Agent的意图模型[J].软件学报, 2000, 11(10):965-970.
[94] Rao A.S, Georgeff M.P. BDI agents from theory to practice [A]. Technical Note 56, AAII[C], 1995.
[95]杨军利,向小军.基于最小成本的飞机着陆规划算法[J].中国民航学院学报, 2006, 24(2):34-36.
[96]周明,孙树栋.遗传算法原理及应用[M].北京:国防工业出版社, 2002.
[97]薛劲松,宋宏. CIMS的总体设计[M].北京:机械工业出版社, 1997.
[98]陈炜炜,耿睿,崔德光.进近区域到达航班排序和调度的优化[J].清华大学学报(自然科学版), 2006, 46(1):157-160.
[99]黎新华,张兆宁.基于Agent的空中交通流量管理系统结构研究[J].交通运输工程与信息学报, 2007, 5(1):56-61.
[100]马正平,崔德光.空中交通战略和战术级流量管理模型[J].清华大学学报, 2003, 43(7):903-907.
[101]罗喜伶.空中交通流量管理系统中关键技术研究[D].北京:北京航空航天大学博士论文, 2002.
[102]赵嶷飞.空中交通流量管理系统研究[D].北京:北京航空航天大学博士论文, 2003.
[103]蔡自兴.人工智能及其应用[M].北京:清华大学出版社, 2004.
[104]史忠植.智能主体及其应用[M].北京:科学出版社, 2000.
[105]张洪海,胡明华,陈世林.基于Agent的终端区流量协同管理研究[C].中国交通研究与探索:第七届全国交通运输领域青年学术会议论文集, 2007:528~532.
[106]王飞.基于Multi-Agent的空中交通协同流量管理[J].广西师范大学学报:自然科学版,2008, 26(10):125-128.