动态不确定环境下航天器观测调度问题研究
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摘要
随着航天观测活动的日益频繁,航天器观测调度问题亦成为学术界和工程界的研究热点之一。该问题的求解对于提高航天活动的收益、降低航天器运行成本和工作人员的劳动强度等均具有重要意义,是未来航天活动不可缺少的一项重要技术。但目前对该问题的研究多局限于静态确定性条件下,对于处于动态不确定环境中的航天器的观测调度问题,缺乏相应地研究。本文在详细分析航天器观测活动背景的基础上,首先建立合理的航天器观测调度问题模型,进而围绕动态不确定环境下航天器观测调度这一核心,从不确定性因素的分析与评估、重调度策略的设计、生成式重调度算法的设计以及修正式重调度算法设计等几个方面,较系统地研究了动态不确定环境下的航天器观测调度问题。具体来说,本文在以下几个方面进行了探索和研究:
一、建立航天器观测调度问题的数学模型。针对目前国内外研究工作中存在的对该问题模型处理的一些弊端,例如对约束条件考虑不足等,在对航天器观测调度过程进行分析的基础上,充分考虑各种约束条件,并进行适当简化,建立多约束条件下的航天器观测调度数学模型,弥补目前研究的不足,为该领域后续工作奠定基础。
二、航天观测中的不确定因素分析与扰动程度评估。在对航天器观测调度活动中的不确定性进行系统总结和分析的基础上,发现一类具有渐变性质的不确定性因素对系统的扰动程度具有模糊性(文中称其为II型渐变性不确定因素),难以对这类不确定性因素对系统造成的扰动程度进行评估和判断,对进一步采取有针对性的处理措施造成了严重阻碍。针对该问题,设计了一种基于模糊神经网络(FNN)的评估算法,用以对此类不确定性因素进行扰动程度评估,为进一步解决动态不确定环境下航天器观测调度问题奠定了基础。
三、提出了一种面向动态不确定环境下航天器观测调度的重调度策略。在上述工作基础上,为了能够对重调度行为进行有效管理,设计了一种基于FNN的改进型混合重调度策略。首先利用模糊神经网络对II型渐变性不确定因素进行评估,决策出相应的重调度方式;在此基础上,引入最小重调度间隔约束,并结合周期性重调度策略与事件驱动型重调度策略,设计了一种改进的混合重调度策略,并进行了算例分析。
四、设计了一种基于自适应混合优化的生成式重调度算法。首先,为了能够将现有优化计算的研究成果应用于航天器观测调度问题的求解,以生成式重调度为背景,在论文前期建立的数学模型基础上,进行了时间窗口离散化和节点表示方法设计两项改进。进而将混合蚁群优化算法引入到该问题的求解当中,并针对算法参数众多、赋值缺乏理论依据的缺陷,提出了一种基于参数自适应的遗传—蚁群混合优化算法(AHACO),并进行了相应的数值仿真和分析。
五、提出了一种基于AHACO的修正式重调度算法。基于对现有调度结果进行局部调整、最大限度利用现有调度信息、以及尽量摆脱具体的不确定性种类和来源的约束、提高重调度算法普适能力的思想,设计了一种基于AHACO的修正式重调度算法。首先对该问题进行了分析,指出进行修正式重调度运算的前提是统计受影响任务,分为直接影响和间接影响两类。进而针对航天器观测调度领域的特点,提出了相应的基于时间约束的受影响任务统计算法。最后设计了一种基于局部信息素调整的修正式重调度算法,并结合上一章的研究成果,将AHACO算法应用到该修正式重调度方法当中。为了检验该方法的有效性,进行了数值仿真和分析。
With the increasing frequency of space exploration, spacecraft observation scheduling has become one of the research focuses in academic and engineering field. Solving this problem could raise the income, reduce the spacecraft running cost or working intensity. It is an indispensable technique for future space activities. However, the main works in this domain are limited within static environment, and lack of relative research and analysis in dynamic environment. With a detailed analysis of the background of spacecraft observation, this dissertation first establishes a reasonable model of spacecraft observation problem. Then, aiming at solving the problem of spacecraft observation rescheduling with uncertainty in dynamic environment, this dissertation systematically studies this problem by analyzing and evaluating the uncertainties, designing the rescheduling policies, generative rescheduling algorithm and repair-based rescheduling algorithm. To be more specific, the main work are as follows:
First, trying to establish a model of spacecraft observation problem. Targeting at the deficiencies in the existing modeling work, such as insufficient consideration about constraints, this dissertation establishes a reasonable model of spacecraft observation problem by bringing constraints in space activities into full consideration and simplifying them moderately in hope of laying some foundations for later work.
Second, analyzing the uncertainties and evaluating the disturbance degree. After analyzing the uncertainties in spacecraft observation scheduling, this dissertation points out that gradual uncertainties have a fuzzy feature (named II type gradual uncertainties in the present dissertation). So it is difficult to evaluate the disturbance degree of these uncertainties. Aiming at solving this problem, this dissertation designs an algorithm based on fuzzy neural network (FNN) to evaluate the disturbance degree of these uncertainties which lays some foundation for further studies.
Third, presenting a policy for spacecraft rescheduling. On the foundation of the above work, in order to effectively manage the rescheduling operations, this dissertation proposes an improved hybrid rescheduling strategy based on FNN. It first evaluates the disturbance degree of II type gradual uncertainties and decides the corresponding rescheduling method. Then, by introducing minimum interval constraint, combined with periodic rescheduling strategy and event-driven rescheduling strategy, the dissertation proposes an improved hybrid rescheduling strategy and provides mathematical analysis.
Fourth, designing a generative rescheduling algorithm based on adaptive hybrid optimization. In order to apply the research achievements of optimization theory to spacecraft observation scheduling, based on generative rescheduling and the established model, the dissertation advances discretization of observing time and node representation, thus brings the hybrid optimization algorithm into the field of spacecraft observation scheduling. In view that the algorithm has too many parameters and it is difficult to set the parameter values, this disseration proposes an adaptive hybrid ant colony optimization algorithm (AHACO) and provides mathematical analysis.
At last, proposing a repair-based rescheduling algorithm using AHACO. In order to make local adjustments to the existing schedule, to maximize the existing schedule information, to be free from the limitations of uncertainty type, and to enhance the general adaptability, the dissertation brings forward the repair-based rescheduling algorithm on AHACO. After analyzing this problem, this dissertation points out that the foundation of this algorithm is to calculate the affected tasks which can be classified into direct and indirect ones. Then, taking into consideration the features of spacecraft observation rescheduling, this dissertation presents a relative statistical algorithm of all affected tasks based on temporal constraint. In the end, this dissertation presents a repair-based rescheduling algorithm based on pheromone local updating, and using the research results of the foregoing chapter, it applies the AHACO to this repair-based rescheduling method and provides mathematical analysis.
一、建立航天器观测调度问题的数学模型。针对目前国内外研究工作中存在的对该问题模型处理的一些弊端,例如对约束条件考虑不足等,在对航天器观测调度过程进行分析的基础上,充分考虑各种约束条件,并进行适当简化,建立多约束条件下的航天器观测调度数学模型,弥补目前研究的不足,为该领域后续工作奠定基础。
二、航天观测中的不确定因素分析与扰动程度评估。在对航天器观测调度活动中的不确定性进行系统总结和分析的基础上,发现一类具有渐变性质的不确定性因素对系统的扰动程度具有模糊性(文中称其为II型渐变性不确定因素),难以对这类不确定性因素对系统造成的扰动程度进行评估和判断,对进一步采取有针对性的处理措施造成了严重阻碍。针对该问题,设计了一种基于模糊神经网络(FNN)的评估算法,用以对此类不确定性因素进行扰动程度评估,为进一步解决动态不确定环境下航天器观测调度问题奠定了基础。
三、提出了一种面向动态不确定环境下航天器观测调度的重调度策略。在上述工作基础上,为了能够对重调度行为进行有效管理,设计了一种基于FNN的改进型混合重调度策略。首先利用模糊神经网络对II型渐变性不确定因素进行评估,决策出相应的重调度方式;在此基础上,引入最小重调度间隔约束,并结合周期性重调度策略与事件驱动型重调度策略,设计了一种改进的混合重调度策略,并进行了算例分析。
四、设计了一种基于自适应混合优化的生成式重调度算法。首先,为了能够将现有优化计算的研究成果应用于航天器观测调度问题的求解,以生成式重调度为背景,在论文前期建立的数学模型基础上,进行了时间窗口离散化和节点表示方法设计两项改进。进而将混合蚁群优化算法引入到该问题的求解当中,并针对算法参数众多、赋值缺乏理论依据的缺陷,提出了一种基于参数自适应的遗传—蚁群混合优化算法(AHACO),并进行了相应的数值仿真和分析。
五、提出了一种基于AHACO的修正式重调度算法。基于对现有调度结果进行局部调整、最大限度利用现有调度信息、以及尽量摆脱具体的不确定性种类和来源的约束、提高重调度算法普适能力的思想,设计了一种基于AHACO的修正式重调度算法。首先对该问题进行了分析,指出进行修正式重调度运算的前提是统计受影响任务,分为直接影响和间接影响两类。进而针对航天器观测调度领域的特点,提出了相应的基于时间约束的受影响任务统计算法。最后设计了一种基于局部信息素调整的修正式重调度算法,并结合上一章的研究成果,将AHACO算法应用到该修正式重调度方法当中。为了检验该方法的有效性,进行了数值仿真和分析。
With the increasing frequency of space exploration, spacecraft observation scheduling has become one of the research focuses in academic and engineering field. Solving this problem could raise the income, reduce the spacecraft running cost or working intensity. It is an indispensable technique for future space activities. However, the main works in this domain are limited within static environment, and lack of relative research and analysis in dynamic environment. With a detailed analysis of the background of spacecraft observation, this dissertation first establishes a reasonable model of spacecraft observation problem. Then, aiming at solving the problem of spacecraft observation rescheduling with uncertainty in dynamic environment, this dissertation systematically studies this problem by analyzing and evaluating the uncertainties, designing the rescheduling policies, generative rescheduling algorithm and repair-based rescheduling algorithm. To be more specific, the main work are as follows:
First, trying to establish a model of spacecraft observation problem. Targeting at the deficiencies in the existing modeling work, such as insufficient consideration about constraints, this dissertation establishes a reasonable model of spacecraft observation problem by bringing constraints in space activities into full consideration and simplifying them moderately in hope of laying some foundations for later work.
Second, analyzing the uncertainties and evaluating the disturbance degree. After analyzing the uncertainties in spacecraft observation scheduling, this dissertation points out that gradual uncertainties have a fuzzy feature (named II type gradual uncertainties in the present dissertation). So it is difficult to evaluate the disturbance degree of these uncertainties. Aiming at solving this problem, this dissertation designs an algorithm based on fuzzy neural network (FNN) to evaluate the disturbance degree of these uncertainties which lays some foundation for further studies.
Third, presenting a policy for spacecraft rescheduling. On the foundation of the above work, in order to effectively manage the rescheduling operations, this dissertation proposes an improved hybrid rescheduling strategy based on FNN. It first evaluates the disturbance degree of II type gradual uncertainties and decides the corresponding rescheduling method. Then, by introducing minimum interval constraint, combined with periodic rescheduling strategy and event-driven rescheduling strategy, the dissertation proposes an improved hybrid rescheduling strategy and provides mathematical analysis.
Fourth, designing a generative rescheduling algorithm based on adaptive hybrid optimization. In order to apply the research achievements of optimization theory to spacecraft observation scheduling, based on generative rescheduling and the established model, the dissertation advances discretization of observing time and node representation, thus brings the hybrid optimization algorithm into the field of spacecraft observation scheduling. In view that the algorithm has too many parameters and it is difficult to set the parameter values, this disseration proposes an adaptive hybrid ant colony optimization algorithm (AHACO) and provides mathematical analysis.
At last, proposing a repair-based rescheduling algorithm using AHACO. In order to make local adjustments to the existing schedule, to maximize the existing schedule information, to be free from the limitations of uncertainty type, and to enhance the general adaptability, the dissertation brings forward the repair-based rescheduling algorithm on AHACO. After analyzing this problem, this dissertation points out that the foundation of this algorithm is to calculate the affected tasks which can be classified into direct and indirect ones. Then, taking into consideration the features of spacecraft observation rescheduling, this dissertation presents a relative statistical algorithm of all affected tasks based on temporal constraint. In the end, this dissertation presents a repair-based rescheduling algorithm based on pheromone local updating, and using the research results of the foregoing chapter, it applies the AHACO to this repair-based rescheduling method and provides mathematical analysis.
引文
1 H. J. Kramer, A. P. Cracknell. An Overview of Small Satellites in Remote Sensing. International Journal of Remote Sensing. 2008,29(15):4285~4337
2刘勇,王秋刚,杜相华.成像侦察卫星及其发展综述.电子对抗. 2005, (6): 39~43
3 Y. Xue, Y. J. Li, J. Guang, et al. Small Satellite Remote Sensing and Applications - History, Current and Future. International Journal of Remote Sensing. 2008,29(15): 4339~4372
4 I. Laszlo, P. Ciren, H. Q. Liu. Remote Sensing of Aerosol and Radiation from Geostationary Satellites. Advances in Space Research. 2008,44(11):1882~1893
5 C. Donlon, I. Robinson, K. S. Casey. The Global Ocean Data Assimilation Experiment High-Resolution Sea Surface. Temperature Pilot Project of the American Meteorological Society. 2007,88(8):1197~1213
6 C. Alexander, S. Gulkis, M Frerking. The U.S. Rosetta Project: Eighteen Months in Flight. IEEE Aerospace Conference Proceedings. Big Sky, MT, United States, 2006:165~178
7 S. Wissler, P. Maldague, J. Rocca, et al. Deep Impact Sequence Planning Using Multi-Mission AdaptablePlanning Tools With Integrated Spacecraft Models, SpaceOps Conference, 2006
8 R. J. Cassady, R. H. Frisbee, J. H. Gilland. Recent Advances in Nuclear Powered Electric Propulsion for Space Exploration. Energy Conversion and Management. 2008,49(3):412~435
9 E. Montagnon, P. Ferri. Rosetta on Its Way to the Outer Solar System. Acta Astronautica. 2006,59(1-5):301~309
10张帆.成像卫星计划编制中的约束建模及优化求解技术研究.长沙,国防科学技术大学博士论文, 2005
11贺仁杰.成像侦察卫星调度问题研究,长沙,国防科学技术大学博士论文, 2004
12 D. J. Lary. Autonomous Objectively Optimized Observing Systems. IEEE International Geoscience and Remote Sensing Symposium. Barcelona, Spain, 2008:1374~1376
13 S. Robin, N Marc. Automated Planning of Science Planetary Missions Reality or Myth. Proceedings of Space Ops 2008, Heidelberg, Germany, 2008
14 S. Marco, R. Miro, S. Klaus. A Scheduling System for Small Ground Station Networks. Proceedings of Space Ops 2008, Heidelberg, Germany, 2008
15 G. Virginie. Strengthened 0-1 Linear Formulation for the Daily Satellite Mission Planning. Journal of Combinatorial Optimization. 2006,11:341~346
16 J. Andrzej. Distance Preserving Recombination Operator for Earth Observation Satellites Operations Scheduling. Journal of Mathematical Modelling and Algorithms. 2008.7:25~42
17 B. D. Smith, B. E. Engelhardt, D. H. Mutz. The RADARSAT-MAMM Automated Mission Planner. AI Magazine, 2002, 23(2):25~36
18 C. Chouinard, R. Knight, G. Jones, et al. Orbital express mission operations planning and resource management using ASPEN. Proceedings of SPIE - The International Society for Optical Engineering, v 6958, Sensors and Systems for Space Applications II. Orlando, FL, United States, 2008:695~706
19 A. Globus, J. Crawford, J. Lohn, et al. A Comparison of Techniques for Scheduling Earth Observing Satellites. In Proceedings of the Sixteenth Innovative Applications of Artificial Intelligence Conference. San Jose, CA, 2004
20 L. Barbulescu, J. P. Watson, L. D. Whitley, et al. Scheduling Space–Ground Communications for the Air Force Satellite Control Network. Journal of Scheduling, 2004, (7):7~34
21 F. David, H. Pierreval, C. Caux. Advanced Planning and Scheduling Systems in Aluminium Conversion Industry. International Journal of Computer Integrated Manufacturing. 2006,19(7):705~715
22 D. Adelman, G. L. Nemhauser, M. Padron, et al. Allocating Fibers in Cable Manufacturing. Manufacturing and Service Operations Management, 1999, 1(1): 21~35
23吴建昱,何小荣,陈丙珍等.新的多产品间歇生产调度的MILP模型.化工学报, 2003, 9:1251~1256
24 B. L. Maccarthy, J. Liu. Addressing the Gap in Scheduling Research:a Review of Optimization and Heuristic Method in Production Scheduling. International Journal of Production Research,1993,31(1):59~79
25 S. S. Panwalkar, W. Iskander. A Survey of Scheduling Rules. Operations Research,1997,25(1): 45~61
26 S. M. Johnson. Optimal Two-and Three-Stage Production Schedules with Set-up Times. Naval Research Logistics Quarterly,1954,1:61~68
27 J. H. Blackstone, D. T. Philips, G. T. Hogg. A State-of-the-Art Survey of Dispatching Rules for Manufacturing Job Shop Operations. International Journal of Production Research, 1982,20(1):27~46
28熊锐,吴澄.车间生产调度问题的技术现状与发展趋势.清华大学学报(自然科学版), 1998, 35(10):55~60
29 M. Fox. Constraint-directed Seareh: A Case Study of Job Shop Seheduling. Carnegie-Mellon University,Pittsburgh,1983
30 X. L. Xu, Y. Tong, X. L. Wang, et al. Production Plan Optimization Assignment Based on Multi-Agent. WSEAS Transactions on Systems. 2006,5(6):1468~1475
31 S. K. Shukla, M. K. Tiwari, Y. J. Son. Bidding-Based Multi-Agent System for Integrated Process Planning and Scheduling: A Data-Mining and Hybrid Tabu-SA Algorithm-Oriented Approach. International Journal of Advanced Manufacturing Technology. 2008,38(1-2):163~175
32 S. F. Smith, M. S. Fox, P. S. Ow. Constructing and Maintaining Detailed Production Plans: Investigations into the Development of Knowledge-based Factory Scheduling Systems. AI Magazine, 1986,7(4):45~61
33 S. Yang, D. Wang. A New Adaptive Neural Network and Heuristics Hybrid Approach for Job-shop Scheduling. Computers & Operations Reasearch, 2001, 28(10):955~971
34 D. Dubois, H. Prade. Fuzzy Constraints in Job Shop Scheduling. Journal of Intelligent Manafacturing, 1995,(6):215~234
35王凌.车间调度及其遗传算法.北京:清华大学出版社, Springer出版社, 2003
36 M. Kolonko. Some New Results on Simulated Annealing Applied to the Job Shop Scheduling Problem. European Journal of Operational Research, 1999, 113:123~136
37 T. Paolo, V. Daniele. The Granular Tabu Search and Its Application to the Vehicle-routing Problem. Informs Journal on Computing. 2003, 15(4):333~346
38 L. A. Zadeh. Fuzzy Sets as a Basis for a Theory of Possibility. Fuzzy Sets and Systems, 1978, 1:3~38
39 S. French. Uncertainty and Imprecision: Modeling and Analysis. Journal of Operations Research Society, 1995, 46(1):70~79
40唐加福,汪定伟.模糊优化理论与方法的研究综述.控制理论与应用, 2000, 17(2) :159~164
41 B. Liu. Uncertain Programming. New York: John Wiley & Sons, 1999
42 S. Nahmisa. Fuzzy Variable. Fuzzy Set and Systems, 1978, 1:97~101
43 B. Liu, Y. K. Liu. Expected Value of Fuzzy Variable and Fuzzy Expected Value Models. IEEE Transaction on Fuzzy Systems, 2003
44 B. Liu. Theory and Practice of Uncertain Programming. Heidelberg: Physica-Verlag, 2002
45王光远.未确知信息的数学处理.哈尔滨建筑工程学院学报, 1990, 4:1~10
46 H. Kwakernaak. Fuzzy Random Variables I. Information Sciences, 1978,
15:1~29
47 H. Kwakernaak. Fuzzy random variables II. Information Sciences, 1979, 17:253~278
48 B. Liu. Uncertain Programming: a Unifying Optimization Theory in Various Uncertain Environments. Applied Mathematics and Computation, 2001, 120(13):227~234
49 J. Gao, B. Liu. New Primitive Vhance Measures of Fuzzy Random Event. International Journal of Fuzzy Systems, 2001, 3(4):527~531
50 H. Ishibuchi, N. Yamatomo, T. Murata, et al. Genetic Algorithms and Neighborhood Research Algorithm for Fuzzy Flowshop Scheduling Problems. Fuzzy Sets and Systems, 1994, 67(1):87~100
51 G. Alvatore, M. Bentto, S. Roman. Rough Sets Theory for Multi Criteria Decision Analysis. European Journal of Operational Research, 2001,129:1~47
52刘亚军,王行愚.基于灰色模拟的灰色机会约束规划.华东理工大学学报, 2003, 29(4):405~408
53 B. P. Qin, X. W. Zhou, J. Yang, et al. Grey-theory Based Intrusion Detection Model. Journal of Systems Engineering and Electronics. 2006, 17(1):230~235
54罗党,刘思峰.灰色动态规划研究.系统工程理论与实践, 2004, 4: 56~62
55刘开第,吴和琴.未确知数学.武汉:华中理工大学出版社, 1997
56刘开第,吴和琴,庞彦军等.不确定性信息数学处理及应用.北京:科学出版社, 1999.7
57岳常安,刘开第.未确知有理数论.石家庄:河北教育出版社, 2001
58杨志民,邓乃扬.未确知机会约束规划.系统工程, 2002, 22(3):11~14
59 E. L. Droguett, A. Mosleh. Bayesian methodology for model uncertainty using model performance data. Risk Analysis. 2008,28(5):1457~1476
60 M. Dyer, L. Stougie. Computational Complexity of Stochastic Programming Problems. Mathematical Programming. 2006,106(3):423~432
61 B. Liu, K. Iwamura. Modelling Stochastic Decision on Systems Using Dependent-chance Programming. European Journal of Operational Research, 1997, 101(1):193~203
62 B. Liu. Dependent-chance Programming in Fuzzy Environments. Fuzzy Sets and Systems, 2000,109(1):97~106
63 B. Liu. Fuzzy Random Dependent-chance Programming. IEEE Transactions on Fuzzy Systems, 2001,9(5):721~726
64 D. Serrano, D. Gossard. Tools and Techniques for Conceptual Design. In: Tong C., Sriram D. eds.. Artificial Intelligence in Engineering Design. New York: Academic Press, 1992:71~116
65 K. Lewis, F. Mistree. Modeling Interactions in Multidisciplinary Design: a Game Theoretic Approach. AIAA Journal, 1997, 35(3):1387~1392
66 C. Lottaz, Y. Robert. Constraint-based for Negotiation in Collaborative Design. Artificial Intelligence in Engineering, 2000, 14(3):261~280
67张少彤,熊光楞,李涛.基于参数协调模型的多学科协同设计方法.计算机学报, 2004, 27(1):115~120
68刘宝旋,赵瑞清,王纲.不确定规划及应用.北京:清华大学出版社, 2003.8
69彭锦,刘宝旋.确定规划的研究现状及其发展前景.管理与运筹, 2002, 11(2):l~10
70 R. Parsons, R. Bennett. Reservoir Operations Management Using a Water Resources Model. Operating Reservoirs in Changing Conditions -Proceedings of the Operations Management 2006 Conference. Sacramento, United States, 2006:p 304~311
71 J. Chen, C. F. Frank. Adaptive Scheduling and Tool Flow Control in Flexible Job Shops. International Journal of Production Research. 2008, 46(15): 4035~4059
72 I. Ferretti, S. Zanoni, L. Zavanella. Production-Inventory Scheduling Using Ant System Metaheuristic. International Journal of Production Economics. 2006,104(2):317~326
73 K. Iwamura, B. Liu. Chance Constrained Integer Programming Models for Capital Budgeting in Fuzzy Environments. Journal of the Operational Research Society, 1998, 49(8):854~860
74 I. Budinska, V. Oravec, B. Frankovic. Central Ontology Layer for Power Grid Scheduling. 2006 IEEE International Conference on Mechatronics. Budapest, Hungary, 2006:267~271
75 F. Alonso, M. J. Alvarez, J. E. Beasley. A Tabu Search Algorithm for The Periodic Vehicle Routing Problem with Multiple Vehicle Trips and Accessibility Restrictions. Journal of the Operational Research Society. 2008,59(7):963~976
76顾幸生,郑璐,李平等.不确定性条件下存储时间有限型Flowshop问题的提前/拖期调度研究.华东理工大学学报, 2004, 30(3):322~327
77顾幸生.不确定性条件下的生产调度.华东理工大学学报, 2002, 26(5) :441~446
78李文华,谭燕秋.不确定性网络直接优化规划模型建立.系统工程理论与实践, 2000, 12:97~101
79施炳利.灰色关联度分析用于水利水电规划方案的选择.水利学报, 1990, 9:66~69
80何勇.灰色多层次综合评判模型及其应用.系统工程理论与实践, 1993, 13(4):72~76
81 E. Balaban, M. Orosz, T. Kichkaylo, et al. Planning to Explore: Using a Coordinated Multisource Infrastructure to Overcome Present and Future Space Flight Planning Challenges. AAAI Spring Symposium, Technical Report. Stanford, United States 2006:9~16
82 B. Roman. Integrating Planning into Production Scheduling: a Formal View.14th International Conference on Automated Planning and Scheduling. Whistler, Canada, 2004:379~386
83 B. D. Minh, K. Subbarao, Z. Terry. Planning-Connections Through Exogenous Events. 14th International Conference on Automated Planning and Scheduling. Whistler, Canada, 2004:1161~1168
84 N. Muscettola. HSTS: Integrated Planning and Scheduling. In M. Zweben, and M Fox, eds. Intelligent Scheduling. Morgan Kaufman. 1994.169~212
85 D. E. Smith, J. Frank, A. K. Jonsson. Bridging the Gap Between Planning and Scheduling. Knowledge Engineering Review, 2000,15(1):166~178
86 W. C. Lin, D. Y. Liao, Y. Liu, et al. Daily Imaging Scheduling of an Earth Observation Satellite. IEEE Transactions on System, Man, and Cybernetics, 2005,35(2):213~223
87 D. Y. Liao, Y. T. Yang. Imaging Order Scheduling of an Earth Observation Satellite. IEEE Transactions on System, Man, and Cybernetics_Part C: Applications and Reviews, 2007,37(5):794~802
88 E. Bensana, G. Verfaillie, N. Bataillie, et al. Exact and Approximate Methods for the Daily Management of an Earth Observing Satellite. Proceedings of SpaceOPS, Germany: Munich, 1996
89 G. Verfaillie, M. Lemaitre. Selecting and Scheduling Observations for Agile Satellites: Some Lessons from the Constraint Reasoning Community Point of View. the Seventh International Conference on Practical and Principles of Constraint Programming, 2001:670~684
90 M. Lemaitre, G. Verfaillie. Daily Management of an Earth Observing Satellite Comparison of ILOG Solver with Dedicated Algorithms for Valued Constraint Satisfaction Problems. Proceedings of 3rd ILOG International Users Meeting, France:Paris, 1997
91 M. Vasques, G. Verfaillie, N. Bataillie. Sharing the Use of a Satellite: an Overview of Methods. Proceeding of SpaceOPS, Japan: Tokyo, 1998
92 M. Lemaitre, G. Verfaillie, F. Jouhaud, et al. Selecting and Scheduling Observations of Agile Satellites. Aerospace Science and Technology, 2002,7:367~381
93 T. Arvidson, S. Goward, J. Gasch, et al. Landsat-7 Long-Term Acquisition Plan: Development and Validation. Photogrammetric Engineering andRemote Sensing. 2006,72(10):1137~1146
94 A. Koratkar, J. Jones, J. Jung, et al. Science Goal Driven Automation for NASA Missions: The Science Goal Monitor. AAAI Spring Symposium, Technical Report. Stanford, United States, 2004:47~52
95 E. Arts, J. K. Lenstra. Local Search in Combinational Optimization. New York: Wiley Press. 1997:25~42
96 L. Barbulescu, J. P. Watson,; L. D. Whitley, et al. Scheduling Space-Ground Communications for the Air Force Satellite Control Network. Journal of Scheduling. 2004,7(1):7~34
97 A. Globus, J. Lohn, R. Morris. Scheduling Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach. Proceedings of the 3rd International NASA Workshop on Planning and Scheduling for Space. NASA, Houston,Texas, 2002:27~29
98 J. Frank, A. Jonsson, R. Morris, et al. Planning and Scheduling for Fleets of Earth Observing Satellites. Proceedings of the 6th International Symposium on Artificial Intelligence, Robotics, Automation and Space 2002, June 2002, Montreal, 18~22
99席政.人工智能在航天飞行任务规划中的应用研究.航空学报, 2007.7, 28(4):791~796
100张利宁,祝江汉,李皓平.多维动态规划在对地观测卫星调度中的应用.计算机仿真, 2007, 24(5):25~29
101阮启明,谭跃进,贺仁杰等.带有活动收益不确定特征的成像侦察卫星调度问题.国防科技大学学报, 2006, 28(2):117~123
102慈元卓,谭跃进,贺仁杰.多星联合对地搜索任务规划技术研究.宇航学报, 2008, 29(2):653~658
103王沛,李菊芳,谭跃进.多星联合对地观测能力评估系统设计与实现.军事运筹与系统工程, 2007, 21(2):68~73
104王军民,谭跃进.多星联合动态调度问题的启发式算法研究.计算机工程与应用, 2007, 43(21):21~25
105何川东.成像卫星计划编制优化决策算法与可视化仿真技术.长沙:国防科技大学,硕士学位论文, 2006
106阮启明.面向区域目标的成像侦察卫星调度问题研究.长沙:国防科技大学,博士学位论文, 2006
107靳肖闪,李军,刘湘辉等.基于拉格朗日松弛与最大分支算法的卫星成像调度算法.宇航学报, 2008, 29(2):694~699
108杨家军.小卫星自主运行的任务规划与调度和故障处理方法的研究.哈尔滨:哈尔滨工业大学,博士学位论文, 1999
109李白.卫星编队任务规划管理分布仿真系统设计实现.哈尔滨:哈尔滨工业大学,硕士学位论文. 2006
110徐瑞.基于多智能体的深空探测器自主任务规划方法与系统实现.哈尔滨:哈尔滨工业大学,博士学位论文. 2004
111徐文明.深空探测器自主任务规划方法研究与系统设计.哈尔滨:哈尔滨工业大学,硕士学位论文. 2006
112陈红波.深空探测器自主规划建模方法研究.哈尔滨:哈尔滨工业大学,硕士学位论文. 2006
113李飞.探测器自主管理系统设计.哈尔滨:哈尔滨工业大学,硕士学位论文. 2007
114代树武,孙辉先.航天器自主运行技术的进展.宇航学报, 2003, 24(1):17~22
115胡圣波,孟新,赵娜.基于模型的航天器自主运行智能执行体.中南大学学报(自然科学版), 2007.8, 38(s1):1061~1065
116王红飞.基于智能Agent的卫星计划协同系统研究.中国科学院研究生院硕士论文, 2006
117王岩.卫星智能自主控制系统的研究.西北工业大学硕士论文, 2004
118周克强.空间飞行器智能自主控制研究.西北工业大学硕士论文, 2006
119陈金勇,冯阳,彭会湘.一种卫星照相规划软件的可视化设计与实现.无线电通信技术, 2004, 30(6):35~37
120 G. Varfaillie, T. Schiex. Solution Reuse in Dynamic Constraint Satisfaction Problem. Proceedings of the Twelfth Conference of the American Association of Artificial Intelligence, 1994:307~312
121 J. Gratch, S. Chien, G. DeJong. Learning Search Control Knowledge for Deep Space Network Scheduling. In Proceedings of the 9th International Conference on Machine Learning. Orlando, USA, 1993:1211~1218
122 J. Gratch, S. Chien. Adaptive Problem-Solving for Large-Scale Scheduling Problems: a Case Study. Journal of Artificial Intelligence Research, 1996 (4):365~396
123 M. D. Johnston. Deep Space Network Scheduling Using Multi-Objective Optimization with Uncertainty. Proceedings of Space Ops 2008, Heidelberg, Germany, 2008
124 J. C. Pemberton, L. G. Greenwald. On the Need for Dynamic Scheduling of the Image Satellite. Pecora15/Land Satellite information IV/ISPRS Commission I/FIEOS 2002 Conference Proceedings, 2002
125 L. Khatib, J. Frank, D. Smith, et al. Interleaved Observation Execution and Rescheduling on Earth Observing Systems. Proceedings of the ICAPS Workshop on Plan Execution, Trento Italy, 2003
126刘洋.成像侦查卫星动态重调度模型、算法及应用研究.国防科技大学博士论文, 2004.10
127高恒振.成像卫星综合任务规划专家决策支持技术研究.国防科技大学硕士论文, 2006
128 G. Rabideau, S. Chien, T. Mann, et al. Interactive, Repair-Based Planning and Scheduling for Shuttle Payload Operations. IEEE Aerospace Conference. 1997:325~341
129 T. Estlin. G. Rabideau. M. Darrem. et al. Using Continuous Planning Techniques to Coordinate Multiple Rovers. IJCAI99 Workshop on Scheduling and Planning, Stockholm, 1999:1~7
130 A. Barrett, R. Knight, R. Morris, et al. Mission Planning and Execution within the Mission Data System. International Workshop on Planning and Scheduling for Space(IWPSS 2004), Darmstadt, Germany, 2004
131 Veridian Inc. GREAS Application Framework Programmer’s Guide. Version 4.2. March, 1999
132 Analytical Graphic Inc. STK/Scheduler Tutorial v3.1. August, 2004
133 W. Potter, J. Gasch. A Photo Album of Earth Scheduling Daily Landsat 7 Activities. Proceedings of Space Ops 98. Tokyo, Japan, 1998
134 J. P. Chamoun, J. Kim, T. Beech, et al. Mission Planning and Scheduling for the Lunar Reconnaissance Orbiter . Proceedings of Space Ops 2008, Heidelberg, Germany, 2008
135 D. C. Mattfeld, C. Bierwirth. An Efficient Genetic Algorithm for Job Shop Scheduling with Tardiness Objectives. European Journal of Operational Research. 2004,155(2):616~630
136 S. J. Honkomp, L. Mockus, G. V. Reklaitis. A Framework for Schedule Evaluation with Processing Uncertainty. Computers and Chemical Engineering, 1999,23:595~609
137李莉,乔非,吴启迪.半导体制造重调度研究.中国机械工程, 2006, 17(6):612~616
138 G. E. Vieira, J. W. Herrmann, E. Lin. Rescheduling Manufacturing Systems: a Framework of Strategies, Policies, and Methods. Journal of Scheduling, 2003,6(1):39~62
139 L. K. Church, R. Uzsoy. Analysis of Periodic and Event Driven Rescheduling Policies in Dynamic Shops. International Journal of Computer Integrated Manufacturing, 1992,5(3):153~163
140许东,吴铮.基于MATLAB 6. x的系统分析与设计—神经网络.西安:西安电子科技大学出版社, 2002: 109~111
141 C. Tran, A. Abraham, L. Jain. A Concurrent Fuzzy-Neural Network Approach for Decision Support Systems. The 12 IEEE International Conference on Fuzzy Systems, 2003, (2):1092~1097
142 G. Singh, K. Singh, K. Singh, et al. Dynamic Scheduling Policy Based on Job Workload Characteristics. WSEAS Transactions on Computers, 2006,5(2):318~323
143 A. Pfeiffer, B. Kadar, L. Monostori, et al. Stability-Oriented Evaluation of Hybrid Rescheduling Methods in a Job-Shop with Machine Breakdowns. 39th CIRP international seminar on manufacturing systems. 2006: 567~586
144 T. N. Wong, C. W. Leung,; K. L. Mak, et al. Dynamic Shopfloor Scheduling in Multi-Agent Manufacturing Systems Expert Systems with Applications. 2006,31(3):486~494
145黎冰,顾幸生.混合规划处理流水车间调度问题.华东理工大学学报(自然科学版). 2006, 1:109~106
146李平,顾幸生.不确定条件下不同交货期窗口的Flow Shop调度系统.仿真学报. 2004,16(1):1287~1292
147 E. N. Pistikopoulos. Uncertainty in Process Design and Operations. Computers and Chemical Engineering, 1995, 19(Sl):553~563
148 H. Chen, D. D. Yao. Dynamic Scheduling Control of a Multi-Class Fluid Network. Operations Research, 1995, 41:1104~1115
149吴受章,辛为民. FMS的模型参考自适应调度.控制理论与应用, 1992, 9(6):646~651
150王朝晖,甘文泉,陈浩勋等.具有模糊缓冲库存约束的化工批处理过程的调度.系统工程理论与实践, 1998, 7:62~68
151 G. E. Vieira, W. H. Jeffrey, L. Edward. Predicting the Performance of Rescheduling Strategies for Parallel Machine Systems. Journal of Manufacturing Systems, 2000, 19(4):256~266
152 J. Fang, Y. Xi. A Rolling Horizon Job Shop Rescheduling Strategy in the Dynamic Environment. International Journal of Advanced Manufacturing Technology, 1997, 13(3):227~232
153 J. W. Herrmann, C. Y. Lee, J. Hinchman. Global Job Shop Scheduling with a Genetic Algorithm. Production and Operations Management. 1995, 4(1): 30~45
154 I. Sabuncuoglu, S. Karabuk. Rescheduling Frequency in a FMS with Uncertain Processing Times and Unreliable Machines. Journal of Manufacturing System, 1999, 18(4): 268~281
155 W. J. Wolfe, S. E. Sorensen. Three Scheduling Algorithms Applied to the Earth Observing Systems Domain. Management Science, 2000, 46(1):148~168
156徐宗本,陈志平.计算机数学——计算复杂性理论与NPC、NP难问题的求解.北京:科学出版社, 2005:119~124
157 C. Blum. Ant colony optimization: Introduction and hybridizations. Proceedings - 7th International Conference on Hybrid Intelligent Systems. Kaiserslautern, Germany, 2007:24~29
158 S. T. Ng, Y. S. Zhang. Optimizing Construction Time and Cost Using Ant Colony Optimization Approach. Journal of Construction Engineering and Management, v 134, n 9, , 2008,134(9):721~728
159 A. Kaveh, S. Shojaee. Optimal Design of Skeletal Structures Using Ant Colony Optimization. International Journal for Numerical Methods in Engineering. 2007,70(5):563~581
160 J. H. Yang, X. H. Shi, M. Marchese, et al. Ant Colony Optimization Method for Generalized TSP Problem. Progress in Natural Science. 2008, 18(11): 1417~1422
161 B. Bontoux, D. Feillet. Ant Colony Optimization for the Traveling Purchaser Problem. Computers and Operations Research, 2008,35(2):628~637
162王小平,曹立明.遗传算法_理论、应用与软件实现.西安:西安交通大学出版社, 2001
163 D. Marco, M. Vittorio, C. Alberto. The Ant System: Optimization by a Colony of Cooperating Agent. IEEE Transactions on Systems Man, and Cybernetics Part B:Cybernetics,1996,26(1):29~41
164 A. Colorni, M. Dorigo, V. Maniezzo. Distributed Optimization by Ant Colonies. In Proceedings of the First European Conference on Artificial Life, Elsevier, 1992, 134~142
165 A. Kaveh, B. Hassani, S. Shojaee, et al. Structural Topology Optimization Using Ant Colony Methodology. Engineering Structures. 2008, 30(9): 2559~2565
166 S. A. Hosseini, Z. Atlasbaf. Optimization of Side Lobe Level and Fixing Quasi-Nulls in Both of the Sum and Difference Patterns By Using Continuous Ant Colony Optimization (ACO) Method. Progress in Electromagnetics Research, 2008,79:321~337
167段海滨.蚁群算法原理及其应用.北京:科学出版社, 2005
168朱庆保,杨志军.基于变异和动态信息素更新的蚁群优化算法.软件学报, 2004, 15(2):185~192
169 J. H. Zhao, Z. H. Liu. Reliability Optimization Using Multiobjective Ant Colony System Approaches. Reliability Engineering and System Safety, v 92, n 1, January, 2007,92(1):109~120
170 M. D. Toksari. Ant Colony Optimization for Finding the Global Minimum. Applied Mathematics and Computation. 2006,176(1):308~316
171 K. Socha, M. Dorigo. Ant Colony Optimization for Continuous Domains. European Journal of Operational Research. 2008,185(3):1155~1173
172王凌.智能优化算法及其应用.北京:清华大学出版社,施普林格出版社, 2001
173 D. H. Wolpert, W.G. Macready. No Free Luneh Theorems for Optimization. IEEE Transactions on Evolutionary Computation,1997,l(1):67~82
174 L. Davis. Hand Book of Genetic Algorithm. NewYork: Van Nostrand Reinhold, 1991
175 I. Tokuda, K. Aihara and T. Nagashima. Adaptive Annealing for Chaotic Optimization. Physical Review E, 1998, 58(4):5157~5160
176石鸿雁,陈治飞,孙昌志.一种混合优化算法及其收敛性证明.控制与决策, 2004, 19(5):546~549
177丁建立,陈增强,袁著祉.遗传算法与蚂蚁算法融合的马尔可夫收敛性分析.自动化学报, 2004, 30(4):629~634
178杨剑锋,蚁群算法及其应用研究.浙江大学博士学位论文. 2007,4
2刘勇,王秋刚,杜相华.成像侦察卫星及其发展综述.电子对抗. 2005, (6): 39~43
3 Y. Xue, Y. J. Li, J. Guang, et al. Small Satellite Remote Sensing and Applications - History, Current and Future. International Journal of Remote Sensing. 2008,29(15): 4339~4372
4 I. Laszlo, P. Ciren, H. Q. Liu. Remote Sensing of Aerosol and Radiation from Geostationary Satellites. Advances in Space Research. 2008,44(11):1882~1893
5 C. Donlon, I. Robinson, K. S. Casey. The Global Ocean Data Assimilation Experiment High-Resolution Sea Surface. Temperature Pilot Project of the American Meteorological Society. 2007,88(8):1197~1213
6 C. Alexander, S. Gulkis, M Frerking. The U.S. Rosetta Project: Eighteen Months in Flight. IEEE Aerospace Conference Proceedings. Big Sky, MT, United States, 2006:165~178
7 S. Wissler, P. Maldague, J. Rocca, et al. Deep Impact Sequence Planning Using Multi-Mission AdaptablePlanning Tools With Integrated Spacecraft Models, SpaceOps Conference, 2006
8 R. J. Cassady, R. H. Frisbee, J. H. Gilland. Recent Advances in Nuclear Powered Electric Propulsion for Space Exploration. Energy Conversion and Management. 2008,49(3):412~435
9 E. Montagnon, P. Ferri. Rosetta on Its Way to the Outer Solar System. Acta Astronautica. 2006,59(1-5):301~309
10张帆.成像卫星计划编制中的约束建模及优化求解技术研究.长沙,国防科学技术大学博士论文, 2005
11贺仁杰.成像侦察卫星调度问题研究,长沙,国防科学技术大学博士论文, 2004
12 D. J. Lary. Autonomous Objectively Optimized Observing Systems. IEEE International Geoscience and Remote Sensing Symposium. Barcelona, Spain, 2008:1374~1376
13 S. Robin, N Marc. Automated Planning of Science Planetary Missions Reality or Myth. Proceedings of Space Ops 2008, Heidelberg, Germany, 2008
14 S. Marco, R. Miro, S. Klaus. A Scheduling System for Small Ground Station Networks. Proceedings of Space Ops 2008, Heidelberg, Germany, 2008
15 G. Virginie. Strengthened 0-1 Linear Formulation for the Daily Satellite Mission Planning. Journal of Combinatorial Optimization. 2006,11:341~346
16 J. Andrzej. Distance Preserving Recombination Operator for Earth Observation Satellites Operations Scheduling. Journal of Mathematical Modelling and Algorithms. 2008.7:25~42
17 B. D. Smith, B. E. Engelhardt, D. H. Mutz. The RADARSAT-MAMM Automated Mission Planner. AI Magazine, 2002, 23(2):25~36
18 C. Chouinard, R. Knight, G. Jones, et al. Orbital express mission operations planning and resource management using ASPEN. Proceedings of SPIE - The International Society for Optical Engineering, v 6958, Sensors and Systems for Space Applications II. Orlando, FL, United States, 2008:695~706
19 A. Globus, J. Crawford, J. Lohn, et al. A Comparison of Techniques for Scheduling Earth Observing Satellites. In Proceedings of the Sixteenth Innovative Applications of Artificial Intelligence Conference. San Jose, CA, 2004
20 L. Barbulescu, J. P. Watson, L. D. Whitley, et al. Scheduling Space–Ground Communications for the Air Force Satellite Control Network. Journal of Scheduling, 2004, (7):7~34
21 F. David, H. Pierreval, C. Caux. Advanced Planning and Scheduling Systems in Aluminium Conversion Industry. International Journal of Computer Integrated Manufacturing. 2006,19(7):705~715
22 D. Adelman, G. L. Nemhauser, M. Padron, et al. Allocating Fibers in Cable Manufacturing. Manufacturing and Service Operations Management, 1999, 1(1): 21~35
23吴建昱,何小荣,陈丙珍等.新的多产品间歇生产调度的MILP模型.化工学报, 2003, 9:1251~1256
24 B. L. Maccarthy, J. Liu. Addressing the Gap in Scheduling Research:a Review of Optimization and Heuristic Method in Production Scheduling. International Journal of Production Research,1993,31(1):59~79
25 S. S. Panwalkar, W. Iskander. A Survey of Scheduling Rules. Operations Research,1997,25(1): 45~61
26 S. M. Johnson. Optimal Two-and Three-Stage Production Schedules with Set-up Times. Naval Research Logistics Quarterly,1954,1:61~68
27 J. H. Blackstone, D. T. Philips, G. T. Hogg. A State-of-the-Art Survey of Dispatching Rules for Manufacturing Job Shop Operations. International Journal of Production Research, 1982,20(1):27~46
28熊锐,吴澄.车间生产调度问题的技术现状与发展趋势.清华大学学报(自然科学版), 1998, 35(10):55~60
29 M. Fox. Constraint-directed Seareh: A Case Study of Job Shop Seheduling. Carnegie-Mellon University,Pittsburgh,1983
30 X. L. Xu, Y. Tong, X. L. Wang, et al. Production Plan Optimization Assignment Based on Multi-Agent. WSEAS Transactions on Systems. 2006,5(6):1468~1475
31 S. K. Shukla, M. K. Tiwari, Y. J. Son. Bidding-Based Multi-Agent System for Integrated Process Planning and Scheduling: A Data-Mining and Hybrid Tabu-SA Algorithm-Oriented Approach. International Journal of Advanced Manufacturing Technology. 2008,38(1-2):163~175
32 S. F. Smith, M. S. Fox, P. S. Ow. Constructing and Maintaining Detailed Production Plans: Investigations into the Development of Knowledge-based Factory Scheduling Systems. AI Magazine, 1986,7(4):45~61
33 S. Yang, D. Wang. A New Adaptive Neural Network and Heuristics Hybrid Approach for Job-shop Scheduling. Computers & Operations Reasearch, 2001, 28(10):955~971
34 D. Dubois, H. Prade. Fuzzy Constraints in Job Shop Scheduling. Journal of Intelligent Manafacturing, 1995,(6):215~234
35王凌.车间调度及其遗传算法.北京:清华大学出版社, Springer出版社, 2003
36 M. Kolonko. Some New Results on Simulated Annealing Applied to the Job Shop Scheduling Problem. European Journal of Operational Research, 1999, 113:123~136
37 T. Paolo, V. Daniele. The Granular Tabu Search and Its Application to the Vehicle-routing Problem. Informs Journal on Computing. 2003, 15(4):333~346
38 L. A. Zadeh. Fuzzy Sets as a Basis for a Theory of Possibility. Fuzzy Sets and Systems, 1978, 1:3~38
39 S. French. Uncertainty and Imprecision: Modeling and Analysis. Journal of Operations Research Society, 1995, 46(1):70~79
40唐加福,汪定伟.模糊优化理论与方法的研究综述.控制理论与应用, 2000, 17(2) :159~164
41 B. Liu. Uncertain Programming. New York: John Wiley & Sons, 1999
42 S. Nahmisa. Fuzzy Variable. Fuzzy Set and Systems, 1978, 1:97~101
43 B. Liu, Y. K. Liu. Expected Value of Fuzzy Variable and Fuzzy Expected Value Models. IEEE Transaction on Fuzzy Systems, 2003
44 B. Liu. Theory and Practice of Uncertain Programming. Heidelberg: Physica-Verlag, 2002
45王光远.未确知信息的数学处理.哈尔滨建筑工程学院学报, 1990, 4:1~10
46 H. Kwakernaak. Fuzzy Random Variables I. Information Sciences, 1978,
15:1~29
47 H. Kwakernaak. Fuzzy random variables II. Information Sciences, 1979, 17:253~278
48 B. Liu. Uncertain Programming: a Unifying Optimization Theory in Various Uncertain Environments. Applied Mathematics and Computation, 2001, 120(13):227~234
49 J. Gao, B. Liu. New Primitive Vhance Measures of Fuzzy Random Event. International Journal of Fuzzy Systems, 2001, 3(4):527~531
50 H. Ishibuchi, N. Yamatomo, T. Murata, et al. Genetic Algorithms and Neighborhood Research Algorithm for Fuzzy Flowshop Scheduling Problems. Fuzzy Sets and Systems, 1994, 67(1):87~100
51 G. Alvatore, M. Bentto, S. Roman. Rough Sets Theory for Multi Criteria Decision Analysis. European Journal of Operational Research, 2001,129:1~47
52刘亚军,王行愚.基于灰色模拟的灰色机会约束规划.华东理工大学学报, 2003, 29(4):405~408
53 B. P. Qin, X. W. Zhou, J. Yang, et al. Grey-theory Based Intrusion Detection Model. Journal of Systems Engineering and Electronics. 2006, 17(1):230~235
54罗党,刘思峰.灰色动态规划研究.系统工程理论与实践, 2004, 4: 56~62
55刘开第,吴和琴.未确知数学.武汉:华中理工大学出版社, 1997
56刘开第,吴和琴,庞彦军等.不确定性信息数学处理及应用.北京:科学出版社, 1999.7
57岳常安,刘开第.未确知有理数论.石家庄:河北教育出版社, 2001
58杨志民,邓乃扬.未确知机会约束规划.系统工程, 2002, 22(3):11~14
59 E. L. Droguett, A. Mosleh. Bayesian methodology for model uncertainty using model performance data. Risk Analysis. 2008,28(5):1457~1476
60 M. Dyer, L. Stougie. Computational Complexity of Stochastic Programming Problems. Mathematical Programming. 2006,106(3):423~432
61 B. Liu, K. Iwamura. Modelling Stochastic Decision on Systems Using Dependent-chance Programming. European Journal of Operational Research, 1997, 101(1):193~203
62 B. Liu. Dependent-chance Programming in Fuzzy Environments. Fuzzy Sets and Systems, 2000,109(1):97~106
63 B. Liu. Fuzzy Random Dependent-chance Programming. IEEE Transactions on Fuzzy Systems, 2001,9(5):721~726
64 D. Serrano, D. Gossard. Tools and Techniques for Conceptual Design. In: Tong C., Sriram D. eds.. Artificial Intelligence in Engineering Design. New York: Academic Press, 1992:71~116
65 K. Lewis, F. Mistree. Modeling Interactions in Multidisciplinary Design: a Game Theoretic Approach. AIAA Journal, 1997, 35(3):1387~1392
66 C. Lottaz, Y. Robert. Constraint-based for Negotiation in Collaborative Design. Artificial Intelligence in Engineering, 2000, 14(3):261~280
67张少彤,熊光楞,李涛.基于参数协调模型的多学科协同设计方法.计算机学报, 2004, 27(1):115~120
68刘宝旋,赵瑞清,王纲.不确定规划及应用.北京:清华大学出版社, 2003.8
69彭锦,刘宝旋.确定规划的研究现状及其发展前景.管理与运筹, 2002, 11(2):l~10
70 R. Parsons, R. Bennett. Reservoir Operations Management Using a Water Resources Model. Operating Reservoirs in Changing Conditions -Proceedings of the Operations Management 2006 Conference. Sacramento, United States, 2006:p 304~311
71 J. Chen, C. F. Frank. Adaptive Scheduling and Tool Flow Control in Flexible Job Shops. International Journal of Production Research. 2008, 46(15): 4035~4059
72 I. Ferretti, S. Zanoni, L. Zavanella. Production-Inventory Scheduling Using Ant System Metaheuristic. International Journal of Production Economics. 2006,104(2):317~326
73 K. Iwamura, B. Liu. Chance Constrained Integer Programming Models for Capital Budgeting in Fuzzy Environments. Journal of the Operational Research Society, 1998, 49(8):854~860
74 I. Budinska, V. Oravec, B. Frankovic. Central Ontology Layer for Power Grid Scheduling. 2006 IEEE International Conference on Mechatronics. Budapest, Hungary, 2006:267~271
75 F. Alonso, M. J. Alvarez, J. E. Beasley. A Tabu Search Algorithm for The Periodic Vehicle Routing Problem with Multiple Vehicle Trips and Accessibility Restrictions. Journal of the Operational Research Society. 2008,59(7):963~976
76顾幸生,郑璐,李平等.不确定性条件下存储时间有限型Flowshop问题的提前/拖期调度研究.华东理工大学学报, 2004, 30(3):322~327
77顾幸生.不确定性条件下的生产调度.华东理工大学学报, 2002, 26(5) :441~446
78李文华,谭燕秋.不确定性网络直接优化规划模型建立.系统工程理论与实践, 2000, 12:97~101
79施炳利.灰色关联度分析用于水利水电规划方案的选择.水利学报, 1990, 9:66~69
80何勇.灰色多层次综合评判模型及其应用.系统工程理论与实践, 1993, 13(4):72~76
81 E. Balaban, M. Orosz, T. Kichkaylo, et al. Planning to Explore: Using a Coordinated Multisource Infrastructure to Overcome Present and Future Space Flight Planning Challenges. AAAI Spring Symposium, Technical Report. Stanford, United States 2006:9~16
82 B. Roman. Integrating Planning into Production Scheduling: a Formal View.14th International Conference on Automated Planning and Scheduling. Whistler, Canada, 2004:379~386
83 B. D. Minh, K. Subbarao, Z. Terry. Planning-Connections Through Exogenous Events. 14th International Conference on Automated Planning and Scheduling. Whistler, Canada, 2004:1161~1168
84 N. Muscettola. HSTS: Integrated Planning and Scheduling. In M. Zweben, and M Fox, eds. Intelligent Scheduling. Morgan Kaufman. 1994.169~212
85 D. E. Smith, J. Frank, A. K. Jonsson. Bridging the Gap Between Planning and Scheduling. Knowledge Engineering Review, 2000,15(1):166~178
86 W. C. Lin, D. Y. Liao, Y. Liu, et al. Daily Imaging Scheduling of an Earth Observation Satellite. IEEE Transactions on System, Man, and Cybernetics, 2005,35(2):213~223
87 D. Y. Liao, Y. T. Yang. Imaging Order Scheduling of an Earth Observation Satellite. IEEE Transactions on System, Man, and Cybernetics_Part C: Applications and Reviews, 2007,37(5):794~802
88 E. Bensana, G. Verfaillie, N. Bataillie, et al. Exact and Approximate Methods for the Daily Management of an Earth Observing Satellite. Proceedings of SpaceOPS, Germany: Munich, 1996
89 G. Verfaillie, M. Lemaitre. Selecting and Scheduling Observations for Agile Satellites: Some Lessons from the Constraint Reasoning Community Point of View. the Seventh International Conference on Practical and Principles of Constraint Programming, 2001:670~684
90 M. Lemaitre, G. Verfaillie. Daily Management of an Earth Observing Satellite Comparison of ILOG Solver with Dedicated Algorithms for Valued Constraint Satisfaction Problems. Proceedings of 3rd ILOG International Users Meeting, France:Paris, 1997
91 M. Vasques, G. Verfaillie, N. Bataillie. Sharing the Use of a Satellite: an Overview of Methods. Proceeding of SpaceOPS, Japan: Tokyo, 1998
92 M. Lemaitre, G. Verfaillie, F. Jouhaud, et al. Selecting and Scheduling Observations of Agile Satellites. Aerospace Science and Technology, 2002,7:367~381
93 T. Arvidson, S. Goward, J. Gasch, et al. Landsat-7 Long-Term Acquisition Plan: Development and Validation. Photogrammetric Engineering andRemote Sensing. 2006,72(10):1137~1146
94 A. Koratkar, J. Jones, J. Jung, et al. Science Goal Driven Automation for NASA Missions: The Science Goal Monitor. AAAI Spring Symposium, Technical Report. Stanford, United States, 2004:47~52
95 E. Arts, J. K. Lenstra. Local Search in Combinational Optimization. New York: Wiley Press. 1997:25~42
96 L. Barbulescu, J. P. Watson,; L. D. Whitley, et al. Scheduling Space-Ground Communications for the Air Force Satellite Control Network. Journal of Scheduling. 2004,7(1):7~34
97 A. Globus, J. Lohn, R. Morris. Scheduling Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach. Proceedings of the 3rd International NASA Workshop on Planning and Scheduling for Space. NASA, Houston,Texas, 2002:27~29
98 J. Frank, A. Jonsson, R. Morris, et al. Planning and Scheduling for Fleets of Earth Observing Satellites. Proceedings of the 6th International Symposium on Artificial Intelligence, Robotics, Automation and Space 2002, June 2002, Montreal, 18~22
99席政.人工智能在航天飞行任务规划中的应用研究.航空学报, 2007.7, 28(4):791~796
100张利宁,祝江汉,李皓平.多维动态规划在对地观测卫星调度中的应用.计算机仿真, 2007, 24(5):25~29
101阮启明,谭跃进,贺仁杰等.带有活动收益不确定特征的成像侦察卫星调度问题.国防科技大学学报, 2006, 28(2):117~123
102慈元卓,谭跃进,贺仁杰.多星联合对地搜索任务规划技术研究.宇航学报, 2008, 29(2):653~658
103王沛,李菊芳,谭跃进.多星联合对地观测能力评估系统设计与实现.军事运筹与系统工程, 2007, 21(2):68~73
104王军民,谭跃进.多星联合动态调度问题的启发式算法研究.计算机工程与应用, 2007, 43(21):21~25
105何川东.成像卫星计划编制优化决策算法与可视化仿真技术.长沙:国防科技大学,硕士学位论文, 2006
106阮启明.面向区域目标的成像侦察卫星调度问题研究.长沙:国防科技大学,博士学位论文, 2006
107靳肖闪,李军,刘湘辉等.基于拉格朗日松弛与最大分支算法的卫星成像调度算法.宇航学报, 2008, 29(2):694~699
108杨家军.小卫星自主运行的任务规划与调度和故障处理方法的研究.哈尔滨:哈尔滨工业大学,博士学位论文, 1999
109李白.卫星编队任务规划管理分布仿真系统设计实现.哈尔滨:哈尔滨工业大学,硕士学位论文. 2006
110徐瑞.基于多智能体的深空探测器自主任务规划方法与系统实现.哈尔滨:哈尔滨工业大学,博士学位论文. 2004
111徐文明.深空探测器自主任务规划方法研究与系统设计.哈尔滨:哈尔滨工业大学,硕士学位论文. 2006
112陈红波.深空探测器自主规划建模方法研究.哈尔滨:哈尔滨工业大学,硕士学位论文. 2006
113李飞.探测器自主管理系统设计.哈尔滨:哈尔滨工业大学,硕士学位论文. 2007
114代树武,孙辉先.航天器自主运行技术的进展.宇航学报, 2003, 24(1):17~22
115胡圣波,孟新,赵娜.基于模型的航天器自主运行智能执行体.中南大学学报(自然科学版), 2007.8, 38(s1):1061~1065
116王红飞.基于智能Agent的卫星计划协同系统研究.中国科学院研究生院硕士论文, 2006
117王岩.卫星智能自主控制系统的研究.西北工业大学硕士论文, 2004
118周克强.空间飞行器智能自主控制研究.西北工业大学硕士论文, 2006
119陈金勇,冯阳,彭会湘.一种卫星照相规划软件的可视化设计与实现.无线电通信技术, 2004, 30(6):35~37
120 G. Varfaillie, T. Schiex. Solution Reuse in Dynamic Constraint Satisfaction Problem. Proceedings of the Twelfth Conference of the American Association of Artificial Intelligence, 1994:307~312
121 J. Gratch, S. Chien, G. DeJong. Learning Search Control Knowledge for Deep Space Network Scheduling. In Proceedings of the 9th International Conference on Machine Learning. Orlando, USA, 1993:1211~1218
122 J. Gratch, S. Chien. Adaptive Problem-Solving for Large-Scale Scheduling Problems: a Case Study. Journal of Artificial Intelligence Research, 1996 (4):365~396
123 M. D. Johnston. Deep Space Network Scheduling Using Multi-Objective Optimization with Uncertainty. Proceedings of Space Ops 2008, Heidelberg, Germany, 2008
124 J. C. Pemberton, L. G. Greenwald. On the Need for Dynamic Scheduling of the Image Satellite. Pecora15/Land Satellite information IV/ISPRS Commission I/FIEOS 2002 Conference Proceedings, 2002
125 L. Khatib, J. Frank, D. Smith, et al. Interleaved Observation Execution and Rescheduling on Earth Observing Systems. Proceedings of the ICAPS Workshop on Plan Execution, Trento Italy, 2003
126刘洋.成像侦查卫星动态重调度模型、算法及应用研究.国防科技大学博士论文, 2004.10
127高恒振.成像卫星综合任务规划专家决策支持技术研究.国防科技大学硕士论文, 2006
128 G. Rabideau, S. Chien, T. Mann, et al. Interactive, Repair-Based Planning and Scheduling for Shuttle Payload Operations. IEEE Aerospace Conference. 1997:325~341
129 T. Estlin. G. Rabideau. M. Darrem. et al. Using Continuous Planning Techniques to Coordinate Multiple Rovers. IJCAI99 Workshop on Scheduling and Planning, Stockholm, 1999:1~7
130 A. Barrett, R. Knight, R. Morris, et al. Mission Planning and Execution within the Mission Data System. International Workshop on Planning and Scheduling for Space(IWPSS 2004), Darmstadt, Germany, 2004
131 Veridian Inc. GREAS Application Framework Programmer’s Guide. Version 4.2. March, 1999
132 Analytical Graphic Inc. STK/Scheduler Tutorial v3.1. August, 2004
133 W. Potter, J. Gasch. A Photo Album of Earth Scheduling Daily Landsat 7 Activities. Proceedings of Space Ops 98. Tokyo, Japan, 1998
134 J. P. Chamoun, J. Kim, T. Beech, et al. Mission Planning and Scheduling for the Lunar Reconnaissance Orbiter . Proceedings of Space Ops 2008, Heidelberg, Germany, 2008
135 D. C. Mattfeld, C. Bierwirth. An Efficient Genetic Algorithm for Job Shop Scheduling with Tardiness Objectives. European Journal of Operational Research. 2004,155(2):616~630
136 S. J. Honkomp, L. Mockus, G. V. Reklaitis. A Framework for Schedule Evaluation with Processing Uncertainty. Computers and Chemical Engineering, 1999,23:595~609
137李莉,乔非,吴启迪.半导体制造重调度研究.中国机械工程, 2006, 17(6):612~616
138 G. E. Vieira, J. W. Herrmann, E. Lin. Rescheduling Manufacturing Systems: a Framework of Strategies, Policies, and Methods. Journal of Scheduling, 2003,6(1):39~62
139 L. K. Church, R. Uzsoy. Analysis of Periodic and Event Driven Rescheduling Policies in Dynamic Shops. International Journal of Computer Integrated Manufacturing, 1992,5(3):153~163
140许东,吴铮.基于MATLAB 6. x的系统分析与设计—神经网络.西安:西安电子科技大学出版社, 2002: 109~111
141 C. Tran, A. Abraham, L. Jain. A Concurrent Fuzzy-Neural Network Approach for Decision Support Systems. The 12 IEEE International Conference on Fuzzy Systems, 2003, (2):1092~1097
142 G. Singh, K. Singh, K. Singh, et al. Dynamic Scheduling Policy Based on Job Workload Characteristics. WSEAS Transactions on Computers, 2006,5(2):318~323
143 A. Pfeiffer, B. Kadar, L. Monostori, et al. Stability-Oriented Evaluation of Hybrid Rescheduling Methods in a Job-Shop with Machine Breakdowns. 39th CIRP international seminar on manufacturing systems. 2006: 567~586
144 T. N. Wong, C. W. Leung,; K. L. Mak, et al. Dynamic Shopfloor Scheduling in Multi-Agent Manufacturing Systems Expert Systems with Applications. 2006,31(3):486~494
145黎冰,顾幸生.混合规划处理流水车间调度问题.华东理工大学学报(自然科学版). 2006, 1:109~106
146李平,顾幸生.不确定条件下不同交货期窗口的Flow Shop调度系统.仿真学报. 2004,16(1):1287~1292
147 E. N. Pistikopoulos. Uncertainty in Process Design and Operations. Computers and Chemical Engineering, 1995, 19(Sl):553~563
148 H. Chen, D. D. Yao. Dynamic Scheduling Control of a Multi-Class Fluid Network. Operations Research, 1995, 41:1104~1115
149吴受章,辛为民. FMS的模型参考自适应调度.控制理论与应用, 1992, 9(6):646~651
150王朝晖,甘文泉,陈浩勋等.具有模糊缓冲库存约束的化工批处理过程的调度.系统工程理论与实践, 1998, 7:62~68
151 G. E. Vieira, W. H. Jeffrey, L. Edward. Predicting the Performance of Rescheduling Strategies for Parallel Machine Systems. Journal of Manufacturing Systems, 2000, 19(4):256~266
152 J. Fang, Y. Xi. A Rolling Horizon Job Shop Rescheduling Strategy in the Dynamic Environment. International Journal of Advanced Manufacturing Technology, 1997, 13(3):227~232
153 J. W. Herrmann, C. Y. Lee, J. Hinchman. Global Job Shop Scheduling with a Genetic Algorithm. Production and Operations Management. 1995, 4(1): 30~45
154 I. Sabuncuoglu, S. Karabuk. Rescheduling Frequency in a FMS with Uncertain Processing Times and Unreliable Machines. Journal of Manufacturing System, 1999, 18(4): 268~281
155 W. J. Wolfe, S. E. Sorensen. Three Scheduling Algorithms Applied to the Earth Observing Systems Domain. Management Science, 2000, 46(1):148~168
156徐宗本,陈志平.计算机数学——计算复杂性理论与NPC、NP难问题的求解.北京:科学出版社, 2005:119~124
157 C. Blum. Ant colony optimization: Introduction and hybridizations. Proceedings - 7th International Conference on Hybrid Intelligent Systems. Kaiserslautern, Germany, 2007:24~29
158 S. T. Ng, Y. S. Zhang. Optimizing Construction Time and Cost Using Ant Colony Optimization Approach. Journal of Construction Engineering and Management, v 134, n 9, , 2008,134(9):721~728
159 A. Kaveh, S. Shojaee. Optimal Design of Skeletal Structures Using Ant Colony Optimization. International Journal for Numerical Methods in Engineering. 2007,70(5):563~581
160 J. H. Yang, X. H. Shi, M. Marchese, et al. Ant Colony Optimization Method for Generalized TSP Problem. Progress in Natural Science. 2008, 18(11): 1417~1422
161 B. Bontoux, D. Feillet. Ant Colony Optimization for the Traveling Purchaser Problem. Computers and Operations Research, 2008,35(2):628~637
162王小平,曹立明.遗传算法_理论、应用与软件实现.西安:西安交通大学出版社, 2001
163 D. Marco, M. Vittorio, C. Alberto. The Ant System: Optimization by a Colony of Cooperating Agent. IEEE Transactions on Systems Man, and Cybernetics Part B:Cybernetics,1996,26(1):29~41
164 A. Colorni, M. Dorigo, V. Maniezzo. Distributed Optimization by Ant Colonies. In Proceedings of the First European Conference on Artificial Life, Elsevier, 1992, 134~142
165 A. Kaveh, B. Hassani, S. Shojaee, et al. Structural Topology Optimization Using Ant Colony Methodology. Engineering Structures. 2008, 30(9): 2559~2565
166 S. A. Hosseini, Z. Atlasbaf. Optimization of Side Lobe Level and Fixing Quasi-Nulls in Both of the Sum and Difference Patterns By Using Continuous Ant Colony Optimization (ACO) Method. Progress in Electromagnetics Research, 2008,79:321~337
167段海滨.蚁群算法原理及其应用.北京:科学出版社, 2005
168朱庆保,杨志军.基于变异和动态信息素更新的蚁群优化算法.软件学报, 2004, 15(2):185~192
169 J. H. Zhao, Z. H. Liu. Reliability Optimization Using Multiobjective Ant Colony System Approaches. Reliability Engineering and System Safety, v 92, n 1, January, 2007,92(1):109~120
170 M. D. Toksari. Ant Colony Optimization for Finding the Global Minimum. Applied Mathematics and Computation. 2006,176(1):308~316
171 K. Socha, M. Dorigo. Ant Colony Optimization for Continuous Domains. European Journal of Operational Research. 2008,185(3):1155~1173
172王凌.智能优化算法及其应用.北京:清华大学出版社,施普林格出版社, 2001
173 D. H. Wolpert, W.G. Macready. No Free Luneh Theorems for Optimization. IEEE Transactions on Evolutionary Computation,1997,l(1):67~82
174 L. Davis. Hand Book of Genetic Algorithm. NewYork: Van Nostrand Reinhold, 1991
175 I. Tokuda, K. Aihara and T. Nagashima. Adaptive Annealing for Chaotic Optimization. Physical Review E, 1998, 58(4):5157~5160
176石鸿雁,陈治飞,孙昌志.一种混合优化算法及其收敛性证明.控制与决策, 2004, 19(5):546~549
177丁建立,陈增强,袁著祉.遗传算法与蚂蚁算法融合的马尔可夫收敛性分析.自动化学报, 2004, 30(4):629~634
178杨剑锋,蚁群算法及其应用研究.浙江大学博士学位论文. 2007,4