基于运行安全的机场停机位分配问题研究
|
摘要
停机位是民用机场中的重要运行设施和资源,为每个进离港的航班选择并分配合理的停机位是机场运行管理中的一项核心任务。现有的停机位分配问题研究主要基于运行效率等方面,对于停机位分配时飞机的运行安全性考虑较少。本文以避免飞机推出冲突为切入点,运用安全管理中防患于未然的管理理念,在概念创新和原有只考虑效率研究的基础上,系统研究了兼顾运行安全和运行效率的停机位分配优化问题。
本论文详细讨论了现有停机位分配相关问题的研究现状,分析了现有研究中的不足之处,指出实现系统运行安全和运行效率双赢是本文研究停机位分配问题的新思维。文章对机场停机位分配问题的研究对象进行了描述,给出了建模需要考虑的约束条件和现有主要优化目标的数学描述,并对影响问题求解的因素和问题求解方法进行了讨论。
在对飞机机坪运行冲突进行分析的基础上,研究了机位资源充足时考虑运行安全的停机位分配问题。提出以“关口前移”的主动避免方法,将限制推出冲突的条件作为安全性约束,建立了推出冲突避免的单目标停机位分配问题新模型。在问题规模较小时,采用整数规划对其进行精确求解;在问题规模增大时,考虑到变量和约束条件数量快速增长带来的组合爆炸问题,设计粗粒度并行遗传算法进行求解。在停机位资源充足的条件下,该模型可达到避免冲突且尽量提高运行效率的目的。
考虑到繁忙机场高峰时段经常出现的停机位资源暂时短缺现象,以及严格约束可能导致的不可解性问题,提出以解除冲突导致的相关旅客等待时间来评价分配方式的运行安全性,建立机坪冲突影响最小化的过约束停机位分配问题新模型,并采用粗粒度并行遗传算法进行求解,得到的优化分配方式可有效降低飞机的邻接机位冲突、机坪滑行冲突,以及停机位资源短缺时分配到同一机位的连续航班对机位利用的冲突等不安全因素的影响。对机位资源充足时的推出冲突避免多目标机位分配问题进行了研究。采用机位空闲时间段的模糊隶属度代替确定性的0-1关系来描述航班-机位之间的匹配程度,并设计基于航班-机位模糊隶属度的调节函数,将分配方式鲁棒性和旅客行走距离最小两个重要优化目标转换为单目标,采用粗粒度并行遗传算法进行优化,在避免推出冲突条件下达到两个相矛盾目标之间的妥协优化。
考虑运行安全和运行效率等目标,研究了过约束的多目标停机位分配问题,优化目标包括基于运行安全的相关旅客等待时间最小,以及旅客模糊行走距离最小、行李模糊搬运距离最小。设计基于Pareto排序的多目标粗粒度并行遗传算法对其进行优化,优化结果可获得一组在Pareto前沿分布均匀的Pareto最优解集合,供决策者选择。
此外,对现有停机位分配问题的主要研究方向进行了分析和改进。分析和设计了预分配方式鲁棒性目标的优化算法;对旅客行走距离最小目标的优化特点进行了分析;对机位资源受限时考虑机型匹配的航班靠桥率最大化优化算法进行了研究;对实时运行中部分航班时刻发生改变的停机位实时再分配问题进行了分析和建模,并采用启发式算法和禁忌搜索算法进行优化。
Gates are important facilities and key resources in civil airport. Selecting and assigning available gate for each arriving and departing flight is a key activity in airport operations. The existing researches on gate assignment problem are mainly based on operational efficiency, while aircraft safety about the gate assignments almost could not be considered in details. Based on conceptual innovation and existing researches on efficiency, this paper will study the airport gate assignment problem (AGAP) which considering both operational safety and efficiency by using the philosophy of“nip in the bud”in safety management.
This thesis discusses AGAP in detail and points out deficiencies of existing researches. New idea of this paper for studying AGAP is achieving the win-win situation of operational safety and efficiency. Airport gates and flights are described. And then constraints and existing main optimal objectives are formulated, and factors affecting problem solving and approaches are discussed. Based on the analysis of the operational process of aircrafts on the apron, gate assignment problem considering operational safety is studied when gates are adequate at first. The proactive approach of“avoid conflict in advance”is adopted by regarding restricted conditions of potential power-in vs. push-out conflict as safety constraints, and then the model with conflict-avoidance is proposed. A mathematical programming technique is used to achieve optimal resolution when the problem scale is small. While the scale is much larger, coarse-grain parallel genetic algorithms(CPGA) is designed to get satisfactory solutions by taking into account the rapid growth of the number of variables and constraints which contribute to combinatorial explosion. The ultimate purpose is enhancing the operational efficiency with avoiding conflict.
Given to the heavy traffic of airports, temporary gate shortages are common phenomenon. Considering the algorithmic unsolvability for gate shortages and strict constraints, minimizing waiting time of passengers to relieve conflicts is proposed as optimization objective, and new model based on operational safety of gate assignment problem is presented. CPGA is adopted to solve the model. The optimization of the model can get the safest assignment while reducing unsafe factors such as conflicts of power-in/push-out, taxiing conflicts in apron, and utilizing conflicts for temporary gate shortages.
Multi-objective optimization model with conflict-avoidance is studied while gates are adequate. The fuzzy membership grades of gate idle periods are used to describe the matching degree of flight-to-gate instead of the determinate 0-1 relationship. Adjustment function on membership degree is introduced to transfer two objectives into one. CPGA is adopted to get a reasonable trade-off between these two conflict objectives based on avoiding conflict.
Over-constrained multi-objective model which considering both operational safety and efficiency is studied. The objevctives inculde minimizing the waiting time of relative passengers based on safety, the fuzzy walking distance of total passengers and the fuzzy baggage transport distance. Multi-objective coarse-grain parallel genetic algorithm is designed to solve the problem. The optimization result can get a set of Pareto solutions which evenly distributed in the Pareto front for airport managers’choice.
In addition, some main objectives of existing gate assignment problem are analyzed and discussed. The algorithm for optimizing the robustness of gate assignment is presented. The optimization characteristic of minimizing the total passengers walking distance is analyzed. The algorithm for maximizing the rate of flight-to-bridge while gates are heterogeneous is developed. The gate reassignment problem is analyzed and modeled while flight schedule are changed, and heuristic algorithm and tabu search algorithm is adopted to optimize the model.
本论文详细讨论了现有停机位分配相关问题的研究现状,分析了现有研究中的不足之处,指出实现系统运行安全和运行效率双赢是本文研究停机位分配问题的新思维。文章对机场停机位分配问题的研究对象进行了描述,给出了建模需要考虑的约束条件和现有主要优化目标的数学描述,并对影响问题求解的因素和问题求解方法进行了讨论。
在对飞机机坪运行冲突进行分析的基础上,研究了机位资源充足时考虑运行安全的停机位分配问题。提出以“关口前移”的主动避免方法,将限制推出冲突的条件作为安全性约束,建立了推出冲突避免的单目标停机位分配问题新模型。在问题规模较小时,采用整数规划对其进行精确求解;在问题规模增大时,考虑到变量和约束条件数量快速增长带来的组合爆炸问题,设计粗粒度并行遗传算法进行求解。在停机位资源充足的条件下,该模型可达到避免冲突且尽量提高运行效率的目的。
考虑到繁忙机场高峰时段经常出现的停机位资源暂时短缺现象,以及严格约束可能导致的不可解性问题,提出以解除冲突导致的相关旅客等待时间来评价分配方式的运行安全性,建立机坪冲突影响最小化的过约束停机位分配问题新模型,并采用粗粒度并行遗传算法进行求解,得到的优化分配方式可有效降低飞机的邻接机位冲突、机坪滑行冲突,以及停机位资源短缺时分配到同一机位的连续航班对机位利用的冲突等不安全因素的影响。对机位资源充足时的推出冲突避免多目标机位分配问题进行了研究。采用机位空闲时间段的模糊隶属度代替确定性的0-1关系来描述航班-机位之间的匹配程度,并设计基于航班-机位模糊隶属度的调节函数,将分配方式鲁棒性和旅客行走距离最小两个重要优化目标转换为单目标,采用粗粒度并行遗传算法进行优化,在避免推出冲突条件下达到两个相矛盾目标之间的妥协优化。
考虑运行安全和运行效率等目标,研究了过约束的多目标停机位分配问题,优化目标包括基于运行安全的相关旅客等待时间最小,以及旅客模糊行走距离最小、行李模糊搬运距离最小。设计基于Pareto排序的多目标粗粒度并行遗传算法对其进行优化,优化结果可获得一组在Pareto前沿分布均匀的Pareto最优解集合,供决策者选择。
此外,对现有停机位分配问题的主要研究方向进行了分析和改进。分析和设计了预分配方式鲁棒性目标的优化算法;对旅客行走距离最小目标的优化特点进行了分析;对机位资源受限时考虑机型匹配的航班靠桥率最大化优化算法进行了研究;对实时运行中部分航班时刻发生改变的停机位实时再分配问题进行了分析和建模,并采用启发式算法和禁忌搜索算法进行优化。
Gates are important facilities and key resources in civil airport. Selecting and assigning available gate for each arriving and departing flight is a key activity in airport operations. The existing researches on gate assignment problem are mainly based on operational efficiency, while aircraft safety about the gate assignments almost could not be considered in details. Based on conceptual innovation and existing researches on efficiency, this paper will study the airport gate assignment problem (AGAP) which considering both operational safety and efficiency by using the philosophy of“nip in the bud”in safety management.
This thesis discusses AGAP in detail and points out deficiencies of existing researches. New idea of this paper for studying AGAP is achieving the win-win situation of operational safety and efficiency. Airport gates and flights are described. And then constraints and existing main optimal objectives are formulated, and factors affecting problem solving and approaches are discussed. Based on the analysis of the operational process of aircrafts on the apron, gate assignment problem considering operational safety is studied when gates are adequate at first. The proactive approach of“avoid conflict in advance”is adopted by regarding restricted conditions of potential power-in vs. push-out conflict as safety constraints, and then the model with conflict-avoidance is proposed. A mathematical programming technique is used to achieve optimal resolution when the problem scale is small. While the scale is much larger, coarse-grain parallel genetic algorithms(CPGA) is designed to get satisfactory solutions by taking into account the rapid growth of the number of variables and constraints which contribute to combinatorial explosion. The ultimate purpose is enhancing the operational efficiency with avoiding conflict.
Given to the heavy traffic of airports, temporary gate shortages are common phenomenon. Considering the algorithmic unsolvability for gate shortages and strict constraints, minimizing waiting time of passengers to relieve conflicts is proposed as optimization objective, and new model based on operational safety of gate assignment problem is presented. CPGA is adopted to solve the model. The optimization of the model can get the safest assignment while reducing unsafe factors such as conflicts of power-in/push-out, taxiing conflicts in apron, and utilizing conflicts for temporary gate shortages.
Multi-objective optimization model with conflict-avoidance is studied while gates are adequate. The fuzzy membership grades of gate idle periods are used to describe the matching degree of flight-to-gate instead of the determinate 0-1 relationship. Adjustment function on membership degree is introduced to transfer two objectives into one. CPGA is adopted to get a reasonable trade-off between these two conflict objectives based on avoiding conflict.
Over-constrained multi-objective model which considering both operational safety and efficiency is studied. The objevctives inculde minimizing the waiting time of relative passengers based on safety, the fuzzy walking distance of total passengers and the fuzzy baggage transport distance. Multi-objective coarse-grain parallel genetic algorithm is designed to solve the problem. The optimization result can get a set of Pareto solutions which evenly distributed in the Pareto front for airport managers’choice.
In addition, some main objectives of existing gate assignment problem are analyzed and discussed. The algorithm for optimizing the robustness of gate assignment is presented. The optimization characteristic of minimizing the total passengers walking distance is analyzed. The algorithm for maximizing the rate of flight-to-bridge while gates are heterogeneous is developed. The gate reassignment problem is analyzed and modeled while flight schedule are changed, and heuristic algorithm and tabu search algorithm is adopted to optimize the model.
引文
[1]中国民用航空总局规划发展司.从统计看民航2009 [M].北京:中国民航出版社, 2009.
[2] Federal Aviation Administration (FAA), Airport capacity benchmark report[R], 2004,http:/www.faa.gov/events/benchmarks/.
[3]张宁.关于交通资源优化配置的几个问题[J],综合运输, 2007, 1(1):8-11.
[4] The International Air Transport Association (IATA), the aviation safety performance for 2009, http://www.freightnet.com/release/4548.htm.
[5] John P. Braaksma,Assoc. Member. Improving airport gate usage with critical path[J], Transportation Engineering Journal, 1971,97(2):187-203.
[6] Babic O., Teodorovic D., Tosic V. Aircraft stand assignment to minimize walking[J], Journal of Transportation Engineering ,1984,110:55-66.
[7] Mangoubi R. S., Dennis F. X. Mathaisel. Optimizing gate assignments at airport terminals[J], Transportation Science, 1985,19(2): 173-188.
[8] Bihr R A. A conceptual solution to the aircraft gate assignment problem using 0,1 linear programming[J], Computerrs and Industrial Engineering, 1990,19: 173-188.
[9] Yan S Y, Chang C M. A network model for gate assignment[J]. Journal of advanced transportation, 1998, 32(2):176-189.
[10] Haghani Ali, Chen Min-Ching. Optimizing gate assignments at airports terminals[J], Transportation Research A, 1998, 32(6): 437-454.
[11] Fariza Zalila. An empirical study of rollout algorithms, beam search and multi-start adaptive memory programming for the airline gate assignment problem[D], America, University of Houston, 2002.
[12] Jiefeng Xu, Bailey G. The airport gate assignment problem: Mathematical model and a tabu search algorithm[C]. In Proceedings of the 34th. Hawaii International Conference on System Sciences, 2001:102-111.
[13] Ding H, A.Lim, B.Rodrigues, Y.Zhu. New heuristics for the over-constrained airport gate assignment problem[J], Journal of the Operational Research Society, 2004, 55:760-768.
[14] Ding H, A.Lim, B.Rodrigues, Y.Zhu. The Airport Gate Assignment Problem[C], In 2003 Decision Science Institute Annual Meeting, DSI-2003, Washington DC-USA.
[15] Ding H, Lim A, Rodrigues B, Zhu Y. The over-constrainted airport gate assignment problem[J]. Computers and Operations Research 2004, 32:1867-1880.
[16] A Lim, B Rodrihues, Yi Zhu. Airport gate scheduling with time windows[J]. Artificial intelligence review, 2005, 24:5-31.
[17]王志清,商红岩,宁宣熙.机场登机口优化调度算法及实证[J],南京航空航天大学学报, 2007, 39(6): 819-823.
[18]王力,刘长有,涂奉生.民用机场停机位优化配置[J],南京航空航天大学学报, 2006,38(4): 433-437.
[19]常钢.民航机场停机位分配与优化技术研究[D],西安:西北工业大学, 2006.
[20]刘长有,卫东选.基于遗传算法的机场停机位分配问题研究[C],第十八届中国控制与决策会议, 2006: 1077-1080
[21]陈欣,陆迅,朱金福.停机位指派模型的排序模拟退火算法[J],应用科学学报, 2007, 25(5): 520-525.
[22]鞠姝妹,许俐.基于GSAA的停机位指派优化问题的研究[J],交通运输系统工程与信息, 2008,8:138-143.
[23] Xiaobing Hu, Ezequiel Di Paolo. Genetic algorithms for the airport gate assignment: linkage, representation and uniform crossover[J], Evolutionary Computation, 2008, SCI 157:361-387.
[24] Xiaobing Hu, DiPaolo E. An E?cient Genetic Algorithm with Uniform Crossover for the Multi-Objective Airport Gate Assignment Problem[C], In Proceedings of 2007 IEEE Congress on Evolutionary Computation, Singapore, 2007:55-62.
[25] A Bolat. Assigning arriving flights at an airport to the available gates[J], Journal of the Operational Research Society, 1999, 50(1): 23-34.
[26] A Bolat, As-Saifan, K. Procedures for aircraft-gate assignment[J], Mathematical & Computational Applications, 1996, 1(1): 9-14.
[27] A Bolat, Assigning Procedures for providing robust gate assignments for arriving aircrafts[J], European Journal of Operational Research, 2000, 120(1): 63-80.
[28] A Bolat, Models and a genetic algorithm for static aircraft-gate assignment problem[J], Journal of the Operational Research Society, 2001, 52(4): 1107-1120.
[29]田晨,熊桂喜.基于遗传算法的机场机位分配策略[J],计算机工程, 2005,31(3):186-189.
[30] Andrew, Fan Wang. Robust airport gate assignment[C], In Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence, 2005.
[31] Hassounah M, Steuart G. Demand for aircraft gates[J], Transportation Research Record, 1993, 1423:26-33.
[32] Shangyao Yan, Cheunming Huo. Optimization of multiple objective gate assignments[J], Transportation Research-A, 2001,35(5):413-432.
[33] Shangyao Yan, Chi-Yuan Shieh, Miawjane Chen. A simulation framework for evaluating airport gate assignments[J], Transportation Research Part A, 2002, 36:885-898.
[34]常钢,魏生民,张建龙.基于多目标规划的停机位分配建模技术研究[J],西北大学学报, 2006,36(5):725-728.
[35] Changyou Liu, Dongxuan Wei. Research on the over-constrained airport gate assignment problem by using GA[C], In Proceeding of 6th World Congress on Intelligent Control and Automation,2006:1304-1140.
[36] Jichen Zhang. A network-flow model for assignment flight to gates at airport[D], Canada:Dalhousie University, 2003.
[37]徐肖豪,张鹏,黄俊祥.基于Memetic算法的机场停机位分配问题研究[J],交通运输工程与信息学报, 2007,5(4):10-17.
[38]王力,刘长有.民用机场停机位优化配置计算机仿真[C].中国控制会议, 2007: 351-355.
[39]王力.民用机场停机位优化配置计算机仿真[J],自动化与仪表, 2007, 4:1-3.
[40] Drexl, A., & Nikulin, Y. Multicriteria airport gate assignment and Pareto simulated annealing[J], IIE Transactions, 2008,40(4):385–397.
[41] Yury Nikulin, Andreas Drexl. Theoretical aspects of multicriteria flight gate scheduling: deterministic and fuzzy models[J], Journal Schedule, 2009, 112(9):26-45.
[42] Yu Gu, Christopher A. Chung. Genetic algorithm approach to aircraft gate reassignment problem[J], Journal of transportation engineering. 2000, 125(5): 384-389.
[43] Shangyao Yan, Ching-Hui Tang. A heuristic approach for airport gate assignments for stochastic flight delays[J], European Journal of Operational Research, 2007, 180:547-567.
[44] Soi-Hoi Lam, Jia-Meng Cao and Henry Fan. Development of an intelligent agent for airport gate assignment[J], Journal of Air Transportation, 2002, 7(2):103-114.
[45] Yu Cheng, A Knowledge-based airport gate assignment system integrated with mathematical programming[J], Computers and industrial engineering, 1997,32(4):837-852.
[46] Yu Cheng, A rule-based reactive model for the simulation of aircraft on airport gates[J], Knowledge-Based Systems, 1998,10: 225-236.
[47] Yu Cheng, Network-Based Simulation of Aircraft at Gates in Airport Terminals[J], Journal of transportation engineering, 1998, 124(2):188-196.
[48] Robert P. Brazile,Kathleen M. Swigger. GATES: An Airline Gate Assignment and Tracking Expert System[J], IEEE EXPERT, 1988, 33-39.
[49]游守田.停机坪及时性调度之研究[D],台湾:成功大学, 2005年.
[50]侯育周.随机性班机到达延误下动态机门指派之研究[D,台湾:中央大学,2007年.
[51] Ulrich Dorndorf, Florian Jaehn, Chen Lin, Hui Ma, Erwin Pesch. Disruption management in flight gate scheduling[J], Statistica Neerlandica, 2007, 61(1):92-114.
[52] Ching-Hui Tang. Real-time gate assignments under temporary gate shortages and stochastic flight delays[C], In 2009 IEEE International Conference on Service Operations, Logistics and Informatics, 2009: 267-271.
[53]朱世群.大型机场机位实时调配问题的研究[D],南京:南京航空航天大学, 2007.
[54]朱世群,朱金福.基于机位预指派的机位实时调配的研究[J],江苏航空, 2006, 4:5-6.
[55]张景杰,陈秋双,孙国华,张倩.基于禁忌搜索算法的停机位应急调度研究[C],中国控制会议, 2007, 84-88.
[56] Singh G K, Christoph M. Preventing runway incursions and conflicts[J], Aerospace Science Technology, 2004,8:653-670
[57] D. F. Green, Runway safety monitor algorithm for runway incursion detection and alerting[J], NASA CR-2002-211416.
[58] C. Quach, Positioning system accuracy assessment for runway incursion prevention system flight test at the Dallas Ft. Worth international airport[R], NASA TM-2004-213506.
[59] D. E. Pitfield, et al., A Monte-Carlo simulation of potentially conflicting ground movements at a new international airport[J], Journal. of Air Transportation Management, 1998, 4: 3-9.
[60] F. David, Runway Safety Monitor Algorithm for Single and Crossing Runway Incursion Detection and Alerting, NASA CR-2002-214275.
[61] Ji Rong, Han Songchen, Route optimizing algorithm of airport surface based on GIS[J], Transaction of Nanjing University of Aeronautics and Astronautics, 2005, 21(1):71-77.
[62]尤杰,韩松臣. MAS在机场路面路径优化中的应用研究[J],交通与计算机, 2008, 26(6):61-64.
[63]张莹,胡明华,王延军.航空器机场地面滑行时刻优化模型研究[J],中国民航飞行学院学报, 2006, 17(5):3-6.
[64] Marín, A. Airport management: taxi planning[J], Annals of Operation Research, 2006, 143: 189–200.
[65]刘刚,朱金福.机坪安全灰色评价方法[J],西南交通大学学学报, 2008, 43(5):600-604.
[66]刘刚,朱金福.基于事故树和动态灰色关联方法的机坪事故人为因素分析研究[J],人类工效学, 2008,14(2):22-25.
[67]赵桂红,田纱纱.基于德尔菲法的机场停机坪安全指标筛选研究[J],中国民航大学学报, 2008, 26(6):61-64.
[68] Yu Cheng. Solving push-out conflicts in apron taxiway by a network based simulation[J],Computer Industrial Engineering, 1998, 2:351-369.
[69]郑攀,胡思继.基于安全性目标的机位分配模型及算法[J],物流技术, 2010, 29(1):51-53.
[70]刘长有,翟乃钧.避免航班推出冲突的多目标停机位优化[C],中国控制会议, 2010, to appear.
[71]朱沛.机场规划与运营管理[M],北京:兵器工业出版社,2003.
[72]朱桥荣.最小化机场机门数量之研究[D].台湾:中央大学,2001.
[73]王力.民用机场停机位的优化配置[D].天津:南开大学. 2009.
[74]文军,孙宏,徐杰,梁志杰.基于排序算法的机场停机位分配问题研究[J],系统工程, 2004, 22(7):102-106.
[75]文军,李冰,王清蓉,杜文.机场停机位分配问题的图着色模型及其算法[J],系统工程理论方法应用, 2005, 14(2):136-140.
[76]罗荣武,谢如鹤,张得志.停机位分配问题的顶点着色模型及算法[J],系统工程理论与实践, 2007, 25(11):148-153.
[77]刑文训,谢金星.现代优化计算方法[M].北京:清华大学出版社, 1999.
[78]玄光南,程润伟.遗传算法与工程优化[M].北京:清华大学出版社, 2004.
[79] E. Cantú-Paz, A survey of parallel genetic algorithms. Calculateurs Parallèles[J], Réseaux et Systèmes Répartis, 1998, 10(2):141–171.
[80] S. C. Lin, W. F. Punch, and E. D. Goodman, Coarse-grain parallel genetic algorithms: Categorization and a new approach[C], In Proceeding 6th IEEE SPDP, 1994:28–37.
[81]曾国荪,丁春玲.并行遗传算法分析[J],计算机工程,2001,27(7):53-55.
[82]卫东选.基于改进遗传算法的机场停机位分配问题研究[D].天津:中国民航大学,2006.
[83]侯广坤,骆江鹏.一种理想并行遗传算法模型[J],软件学报, 1999,10(5): 557-560.
[84]戴晓明,许超,龚向阳,邵惠鹤.并行遗传算法收敛性分析及优化运算[J],计算机工程,2002,28(6):92-95.
[85] Prahlada Rao B B, Hansdah R C. Extended distributed genetic algorithm for channel routing[C], In Procceding of 5th IEEE Symp. on Parallel and Distributed Processing, Los Alam itos, CA, 1993: 726-733.
[86] Calegari P, Guidec F, Kuonen, P, et al. Parallel island-based genetic algorithm for radio network design [J], Jounaral Parallel Distrib. Comput, 1997, 47 (1) : 86- 90.
[87] Topping B H, Sziveri J, Bahreinejad A, et al. Parallel processing, neural networks and genetic algorithms[J], Advaces in Engineering Software, 1998, 29 (10) : 763- 786.
[88] Udo Kohlmorgen, Hartmut Schmeck, Knut Haase. Experiences with fine-grained parallel genetic algorithms[J], Annals of Operations Research, 1999, 90:203-219.
[89] Erick Cantu-Paz, David E Goldberg. Efficient parallel genetic algorithms: theory and practice[J], Computer methods in applied mechanics and engineering, 2000,186:221-238.
[90]谢胜利,唐敏,董金祥.求解TSP问题的一种改进的遗传算法[J],计算机工程与应用, 2002, 58(8): 58-60.
[91]杨宁,田蔚风,金志华.一种求解旅行商问题的交叉禁忌搜索[J],系统仿真学报, 2006, 18 (4) : 897-900.
[92] Zadeh L A. Fuzzy sets[J], Information and Control, 1965, 8:338-353.
[93] Coello Coello CA. A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques[J], Knowledge and Information Systems, 1999, 1(3):269–308.
[94] Coello Coello CA. 20 years of evolutionary multi-objective optimization: what has been done and what remains to be done. In Computartional Intelligence: Principles and Practice[C], In IEEE Computational Intelligence Society. 2006, 73-88.
[95] Coello Coello CA. Evolutionary multiobjective optimization: A historical view of the field[J], IEEE Computational Intelligence Magazine, 2006, 1(1):28–36.
[96] Coello Coello CA. Evolutionary multi-objective optimization: some current research trends and topics that remain to be explored[J], Computer Science of China, 2009,3(1):18-30.
[97] Eckart Zitzler, Lothar Thiele. Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach[J], IEEE Transactions on Evolutionary Computation, 1999,3(4):257–271.
[98] Zitzler E., Thiele L., Deb K.. Comparison of multiobjective evolutionary algorithms: Empirical results[J], Evolutionary Computation, 2000,8(2):173–195.
[99] Deb K. Current trends in evolutionary multi-objective optimization[J]. Journal Simulation Multi-discussion. Des. Optimization, 2007,1(1):1-8.
[100]郑向伟,刘弘.多目标进化算法研究进展[J],计算机科学, 2007, 34(7):187-192.
[101]唐云岚,赵青松,高妍方,陈英武. Pareto最优概念的多目标进化算法综述[J],计算机科学, 2008,35(10):25-28.
[102]公茂果,焦李成,杨咚咚,马文萍.进化多目标优化算法研究[J],软件学报, 2009,20(2): 271-289.
[103]崔逊学.多目标进化算法及其应用[M].北京:国防工业出版社,2006.
[104]郑金华.多目标进化算法及其应用[M].北京:科学出版社,2007.
[105] Schaffer J D. Some experiments in machine learning using vector evaluated genetic algorithms [D]. America, Vanderbilt University, 1984.
[106] Fonseca Carlos M, Peter J Fleming. Genetic algorithm for multiobjective optimization:Formulation, discussion and generalization[C]. In Proceedings of the Fifth International Conference on Genetic Algorithms, 1993: 416-423.
[107] Srinivas N, Kalynamoy Deb. Multiobjective optimization using nondominated sorting in genetic algorithms[J]. Evoluationary Computation, 1994, 2(3): 221-248.
[108] Zitzler E, Thiele L. Multi-objective evolutionary algorithms: a comparative case study and the strength pareto approach[J]. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257-271.
[109] Zitzler E, Laumanns M, Thiele L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm[C]. In Giannakoglou K, et al. eds EUROGEN 2001. Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, Athens, Greece, 2002: 95-100.
[110] Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multi-objective genetic algorithm: NSGA-II[J], IEEE Transactions on evolutionary computation, 2002, 6(2):182-197.
[2] Federal Aviation Administration (FAA), Airport capacity benchmark report[R], 2004,http:/www.faa.gov/events/benchmarks/.
[3]张宁.关于交通资源优化配置的几个问题[J],综合运输, 2007, 1(1):8-11.
[4] The International Air Transport Association (IATA), the aviation safety performance for 2009, http://www.freightnet.com/release/4548.htm.
[5] John P. Braaksma,Assoc. Member. Improving airport gate usage with critical path[J], Transportation Engineering Journal, 1971,97(2):187-203.
[6] Babic O., Teodorovic D., Tosic V. Aircraft stand assignment to minimize walking[J], Journal of Transportation Engineering ,1984,110:55-66.
[7] Mangoubi R. S., Dennis F. X. Mathaisel. Optimizing gate assignments at airport terminals[J], Transportation Science, 1985,19(2): 173-188.
[8] Bihr R A. A conceptual solution to the aircraft gate assignment problem using 0,1 linear programming[J], Computerrs and Industrial Engineering, 1990,19: 173-188.
[9] Yan S Y, Chang C M. A network model for gate assignment[J]. Journal of advanced transportation, 1998, 32(2):176-189.
[10] Haghani Ali, Chen Min-Ching. Optimizing gate assignments at airports terminals[J], Transportation Research A, 1998, 32(6): 437-454.
[11] Fariza Zalila. An empirical study of rollout algorithms, beam search and multi-start adaptive memory programming for the airline gate assignment problem[D], America, University of Houston, 2002.
[12] Jiefeng Xu, Bailey G. The airport gate assignment problem: Mathematical model and a tabu search algorithm[C]. In Proceedings of the 34th. Hawaii International Conference on System Sciences, 2001:102-111.
[13] Ding H, A.Lim, B.Rodrigues, Y.Zhu. New heuristics for the over-constrained airport gate assignment problem[J], Journal of the Operational Research Society, 2004, 55:760-768.
[14] Ding H, A.Lim, B.Rodrigues, Y.Zhu. The Airport Gate Assignment Problem[C], In 2003 Decision Science Institute Annual Meeting, DSI-2003, Washington DC-USA.
[15] Ding H, Lim A, Rodrigues B, Zhu Y. The over-constrainted airport gate assignment problem[J]. Computers and Operations Research 2004, 32:1867-1880.
[16] A Lim, B Rodrihues, Yi Zhu. Airport gate scheduling with time windows[J]. Artificial intelligence review, 2005, 24:5-31.
[17]王志清,商红岩,宁宣熙.机场登机口优化调度算法及实证[J],南京航空航天大学学报, 2007, 39(6): 819-823.
[18]王力,刘长有,涂奉生.民用机场停机位优化配置[J],南京航空航天大学学报, 2006,38(4): 433-437.
[19]常钢.民航机场停机位分配与优化技术研究[D],西安:西北工业大学, 2006.
[20]刘长有,卫东选.基于遗传算法的机场停机位分配问题研究[C],第十八届中国控制与决策会议, 2006: 1077-1080
[21]陈欣,陆迅,朱金福.停机位指派模型的排序模拟退火算法[J],应用科学学报, 2007, 25(5): 520-525.
[22]鞠姝妹,许俐.基于GSAA的停机位指派优化问题的研究[J],交通运输系统工程与信息, 2008,8:138-143.
[23] Xiaobing Hu, Ezequiel Di Paolo. Genetic algorithms for the airport gate assignment: linkage, representation and uniform crossover[J], Evolutionary Computation, 2008, SCI 157:361-387.
[24] Xiaobing Hu, DiPaolo E. An E?cient Genetic Algorithm with Uniform Crossover for the Multi-Objective Airport Gate Assignment Problem[C], In Proceedings of 2007 IEEE Congress on Evolutionary Computation, Singapore, 2007:55-62.
[25] A Bolat. Assigning arriving flights at an airport to the available gates[J], Journal of the Operational Research Society, 1999, 50(1): 23-34.
[26] A Bolat, As-Saifan, K. Procedures for aircraft-gate assignment[J], Mathematical & Computational Applications, 1996, 1(1): 9-14.
[27] A Bolat, Assigning Procedures for providing robust gate assignments for arriving aircrafts[J], European Journal of Operational Research, 2000, 120(1): 63-80.
[28] A Bolat, Models and a genetic algorithm for static aircraft-gate assignment problem[J], Journal of the Operational Research Society, 2001, 52(4): 1107-1120.
[29]田晨,熊桂喜.基于遗传算法的机场机位分配策略[J],计算机工程, 2005,31(3):186-189.
[30] Andrew, Fan Wang. Robust airport gate assignment[C], In Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence, 2005.
[31] Hassounah M, Steuart G. Demand for aircraft gates[J], Transportation Research Record, 1993, 1423:26-33.
[32] Shangyao Yan, Cheunming Huo. Optimization of multiple objective gate assignments[J], Transportation Research-A, 2001,35(5):413-432.
[33] Shangyao Yan, Chi-Yuan Shieh, Miawjane Chen. A simulation framework for evaluating airport gate assignments[J], Transportation Research Part A, 2002, 36:885-898.
[34]常钢,魏生民,张建龙.基于多目标规划的停机位分配建模技术研究[J],西北大学学报, 2006,36(5):725-728.
[35] Changyou Liu, Dongxuan Wei. Research on the over-constrained airport gate assignment problem by using GA[C], In Proceeding of 6th World Congress on Intelligent Control and Automation,2006:1304-1140.
[36] Jichen Zhang. A network-flow model for assignment flight to gates at airport[D], Canada:Dalhousie University, 2003.
[37]徐肖豪,张鹏,黄俊祥.基于Memetic算法的机场停机位分配问题研究[J],交通运输工程与信息学报, 2007,5(4):10-17.
[38]王力,刘长有.民用机场停机位优化配置计算机仿真[C].中国控制会议, 2007: 351-355.
[39]王力.民用机场停机位优化配置计算机仿真[J],自动化与仪表, 2007, 4:1-3.
[40] Drexl, A., & Nikulin, Y. Multicriteria airport gate assignment and Pareto simulated annealing[J], IIE Transactions, 2008,40(4):385–397.
[41] Yury Nikulin, Andreas Drexl. Theoretical aspects of multicriteria flight gate scheduling: deterministic and fuzzy models[J], Journal Schedule, 2009, 112(9):26-45.
[42] Yu Gu, Christopher A. Chung. Genetic algorithm approach to aircraft gate reassignment problem[J], Journal of transportation engineering. 2000, 125(5): 384-389.
[43] Shangyao Yan, Ching-Hui Tang. A heuristic approach for airport gate assignments for stochastic flight delays[J], European Journal of Operational Research, 2007, 180:547-567.
[44] Soi-Hoi Lam, Jia-Meng Cao and Henry Fan. Development of an intelligent agent for airport gate assignment[J], Journal of Air Transportation, 2002, 7(2):103-114.
[45] Yu Cheng, A Knowledge-based airport gate assignment system integrated with mathematical programming[J], Computers and industrial engineering, 1997,32(4):837-852.
[46] Yu Cheng, A rule-based reactive model for the simulation of aircraft on airport gates[J], Knowledge-Based Systems, 1998,10: 225-236.
[47] Yu Cheng, Network-Based Simulation of Aircraft at Gates in Airport Terminals[J], Journal of transportation engineering, 1998, 124(2):188-196.
[48] Robert P. Brazile,Kathleen M. Swigger. GATES: An Airline Gate Assignment and Tracking Expert System[J], IEEE EXPERT, 1988, 33-39.
[49]游守田.停机坪及时性调度之研究[D],台湾:成功大学, 2005年.
[50]侯育周.随机性班机到达延误下动态机门指派之研究[D,台湾:中央大学,2007年.
[51] Ulrich Dorndorf, Florian Jaehn, Chen Lin, Hui Ma, Erwin Pesch. Disruption management in flight gate scheduling[J], Statistica Neerlandica, 2007, 61(1):92-114.
[52] Ching-Hui Tang. Real-time gate assignments under temporary gate shortages and stochastic flight delays[C], In 2009 IEEE International Conference on Service Operations, Logistics and Informatics, 2009: 267-271.
[53]朱世群.大型机场机位实时调配问题的研究[D],南京:南京航空航天大学, 2007.
[54]朱世群,朱金福.基于机位预指派的机位实时调配的研究[J],江苏航空, 2006, 4:5-6.
[55]张景杰,陈秋双,孙国华,张倩.基于禁忌搜索算法的停机位应急调度研究[C],中国控制会议, 2007, 84-88.
[56] Singh G K, Christoph M. Preventing runway incursions and conflicts[J], Aerospace Science Technology, 2004,8:653-670
[57] D. F. Green, Runway safety monitor algorithm for runway incursion detection and alerting[J], NASA CR-2002-211416.
[58] C. Quach, Positioning system accuracy assessment for runway incursion prevention system flight test at the Dallas Ft. Worth international airport[R], NASA TM-2004-213506.
[59] D. E. Pitfield, et al., A Monte-Carlo simulation of potentially conflicting ground movements at a new international airport[J], Journal. of Air Transportation Management, 1998, 4: 3-9.
[60] F. David, Runway Safety Monitor Algorithm for Single and Crossing Runway Incursion Detection and Alerting, NASA CR-2002-214275.
[61] Ji Rong, Han Songchen, Route optimizing algorithm of airport surface based on GIS[J], Transaction of Nanjing University of Aeronautics and Astronautics, 2005, 21(1):71-77.
[62]尤杰,韩松臣. MAS在机场路面路径优化中的应用研究[J],交通与计算机, 2008, 26(6):61-64.
[63]张莹,胡明华,王延军.航空器机场地面滑行时刻优化模型研究[J],中国民航飞行学院学报, 2006, 17(5):3-6.
[64] Marín, A. Airport management: taxi planning[J], Annals of Operation Research, 2006, 143: 189–200.
[65]刘刚,朱金福.机坪安全灰色评价方法[J],西南交通大学学学报, 2008, 43(5):600-604.
[66]刘刚,朱金福.基于事故树和动态灰色关联方法的机坪事故人为因素分析研究[J],人类工效学, 2008,14(2):22-25.
[67]赵桂红,田纱纱.基于德尔菲法的机场停机坪安全指标筛选研究[J],中国民航大学学报, 2008, 26(6):61-64.
[68] Yu Cheng. Solving push-out conflicts in apron taxiway by a network based simulation[J],Computer Industrial Engineering, 1998, 2:351-369.
[69]郑攀,胡思继.基于安全性目标的机位分配模型及算法[J],物流技术, 2010, 29(1):51-53.
[70]刘长有,翟乃钧.避免航班推出冲突的多目标停机位优化[C],中国控制会议, 2010, to appear.
[71]朱沛.机场规划与运营管理[M],北京:兵器工业出版社,2003.
[72]朱桥荣.最小化机场机门数量之研究[D].台湾:中央大学,2001.
[73]王力.民用机场停机位的优化配置[D].天津:南开大学. 2009.
[74]文军,孙宏,徐杰,梁志杰.基于排序算法的机场停机位分配问题研究[J],系统工程, 2004, 22(7):102-106.
[75]文军,李冰,王清蓉,杜文.机场停机位分配问题的图着色模型及其算法[J],系统工程理论方法应用, 2005, 14(2):136-140.
[76]罗荣武,谢如鹤,张得志.停机位分配问题的顶点着色模型及算法[J],系统工程理论与实践, 2007, 25(11):148-153.
[77]刑文训,谢金星.现代优化计算方法[M].北京:清华大学出版社, 1999.
[78]玄光南,程润伟.遗传算法与工程优化[M].北京:清华大学出版社, 2004.
[79] E. Cantú-Paz, A survey of parallel genetic algorithms. Calculateurs Parallèles[J], Réseaux et Systèmes Répartis, 1998, 10(2):141–171.
[80] S. C. Lin, W. F. Punch, and E. D. Goodman, Coarse-grain parallel genetic algorithms: Categorization and a new approach[C], In Proceeding 6th IEEE SPDP, 1994:28–37.
[81]曾国荪,丁春玲.并行遗传算法分析[J],计算机工程,2001,27(7):53-55.
[82]卫东选.基于改进遗传算法的机场停机位分配问题研究[D].天津:中国民航大学,2006.
[83]侯广坤,骆江鹏.一种理想并行遗传算法模型[J],软件学报, 1999,10(5): 557-560.
[84]戴晓明,许超,龚向阳,邵惠鹤.并行遗传算法收敛性分析及优化运算[J],计算机工程,2002,28(6):92-95.
[85] Prahlada Rao B B, Hansdah R C. Extended distributed genetic algorithm for channel routing[C], In Procceding of 5th IEEE Symp. on Parallel and Distributed Processing, Los Alam itos, CA, 1993: 726-733.
[86] Calegari P, Guidec F, Kuonen, P, et al. Parallel island-based genetic algorithm for radio network design [J], Jounaral Parallel Distrib. Comput, 1997, 47 (1) : 86- 90.
[87] Topping B H, Sziveri J, Bahreinejad A, et al. Parallel processing, neural networks and genetic algorithms[J], Advaces in Engineering Software, 1998, 29 (10) : 763- 786.
[88] Udo Kohlmorgen, Hartmut Schmeck, Knut Haase. Experiences with fine-grained parallel genetic algorithms[J], Annals of Operations Research, 1999, 90:203-219.
[89] Erick Cantu-Paz, David E Goldberg. Efficient parallel genetic algorithms: theory and practice[J], Computer methods in applied mechanics and engineering, 2000,186:221-238.
[90]谢胜利,唐敏,董金祥.求解TSP问题的一种改进的遗传算法[J],计算机工程与应用, 2002, 58(8): 58-60.
[91]杨宁,田蔚风,金志华.一种求解旅行商问题的交叉禁忌搜索[J],系统仿真学报, 2006, 18 (4) : 897-900.
[92] Zadeh L A. Fuzzy sets[J], Information and Control, 1965, 8:338-353.
[93] Coello Coello CA. A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques[J], Knowledge and Information Systems, 1999, 1(3):269–308.
[94] Coello Coello CA. 20 years of evolutionary multi-objective optimization: what has been done and what remains to be done. In Computartional Intelligence: Principles and Practice[C], In IEEE Computational Intelligence Society. 2006, 73-88.
[95] Coello Coello CA. Evolutionary multiobjective optimization: A historical view of the field[J], IEEE Computational Intelligence Magazine, 2006, 1(1):28–36.
[96] Coello Coello CA. Evolutionary multi-objective optimization: some current research trends and topics that remain to be explored[J], Computer Science of China, 2009,3(1):18-30.
[97] Eckart Zitzler, Lothar Thiele. Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach[J], IEEE Transactions on Evolutionary Computation, 1999,3(4):257–271.
[98] Zitzler E., Thiele L., Deb K.. Comparison of multiobjective evolutionary algorithms: Empirical results[J], Evolutionary Computation, 2000,8(2):173–195.
[99] Deb K. Current trends in evolutionary multi-objective optimization[J]. Journal Simulation Multi-discussion. Des. Optimization, 2007,1(1):1-8.
[100]郑向伟,刘弘.多目标进化算法研究进展[J],计算机科学, 2007, 34(7):187-192.
[101]唐云岚,赵青松,高妍方,陈英武. Pareto最优概念的多目标进化算法综述[J],计算机科学, 2008,35(10):25-28.
[102]公茂果,焦李成,杨咚咚,马文萍.进化多目标优化算法研究[J],软件学报, 2009,20(2): 271-289.
[103]崔逊学.多目标进化算法及其应用[M].北京:国防工业出版社,2006.
[104]郑金华.多目标进化算法及其应用[M].北京:科学出版社,2007.
[105] Schaffer J D. Some experiments in machine learning using vector evaluated genetic algorithms [D]. America, Vanderbilt University, 1984.
[106] Fonseca Carlos M, Peter J Fleming. Genetic algorithm for multiobjective optimization:Formulation, discussion and generalization[C]. In Proceedings of the Fifth International Conference on Genetic Algorithms, 1993: 416-423.
[107] Srinivas N, Kalynamoy Deb. Multiobjective optimization using nondominated sorting in genetic algorithms[J]. Evoluationary Computation, 1994, 2(3): 221-248.
[108] Zitzler E, Thiele L. Multi-objective evolutionary algorithms: a comparative case study and the strength pareto approach[J]. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257-271.
[109] Zitzler E, Laumanns M, Thiele L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm[C]. In Giannakoglou K, et al. eds EUROGEN 2001. Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, Athens, Greece, 2002: 95-100.
[110] Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multi-objective genetic algorithm: NSGA-II[J], IEEE Transactions on evolutionary computation, 2002, 6(2):182-197.