成像卫星星地综合调度技术研究
|
摘要
卫星对地成像是获取地球表面信息的一种重要手段。当前社会经济和军事等领域的对地成像应用体现出成像区域分布广泛、观测频繁、成像方式多样、成像时效性强等特点,带来对地成像需求的快速增长。随着以多种类型成像卫星、地面接收站和中继卫星系统等组成的对地成像系统的发展,作为对地成像系统控制核心的控制中心需要从全局角度分配成像卫星和接收资源来进行星地资源的综合调度,以最大程度满足对地成像需求。然而,目前卫星对地成像调度研究大多集中在成像阶段的调度或接收阶段的调度,缺乏将成像过程和接收过程进行整体优化考虑的方法和技术。
论文提出了星地资源综合调度的思想来解决卫星成像过程和数据接收过程的整体优化问题,以星地综合调度模型为基础,重点研究了基于置换表示的调度优化技术,并通过松弛优化方法验证了所提方法的有效性。主要工作和创新点包括:
1、建立了包含多类型星地资源的星地综合调度数学模型。
星地综合调度模型是星地综合调度研究的基础。论文分析了成像卫星和接收资源的工作特性,研究了星地综合调度问题的组成要素及其相互关系,提出了将成像活动和接收活动进行组合的星地综合调度数学模型,并可适应调度周期的变化。
2、提出了基于置换表示的星地综合调度问题可行解表示方法。
星地综合调度问题是非线性复杂约束优化问题,直接在问题空间上进行搜索时可行调度的构造和优化难以实现。为此,论文研究了基于置换表示的可行解表示方法,通过为置换序列分配星地资源,将可行调度优化转化为无约束空间上的搜索问题。
3、研究了两类可行调度优化算法。
最优调度的搜索由在置换空间上搜索具有最大评价值置换序列的过程实现。论文基于邻域图和对称群理论,分析了置换空间上交换邻域和插入邻域的性质,提出了具有简单结构的有记忆随机邻域搜索算法,可适应对调度优化时间的需要。为了获得更优化的调度,论文提出了一种混合遗传算法,在保留全局收敛性的同时,以邻域搜索算法增强了遗传算法的局部寻优能力。
4、研究了基于拉格朗日松弛的星地综合调度可行解优化评价方法。
星地综合调度是一类NP难解的组合最优化问题,优化算法只能在可接受花费下给出该问题的近似最优解,但所得可行解的优化程度需要其它优化方法来评价。论文基于具有多项式复杂度的最长路径算法和次梯度优化算法求解星地综合调度松弛问题,以获得原问题的紧致上界。实验结果显示,基于置换表示方式的可行解优化算法,所得可行解非常接近松弛方法所得上界,证明了论文提出的星地综合调度优化方法具有较高的优化能力,可以满足星地综合调度问题的优化需求。
基于以上研究成果,论文最后设计并实现了星地综合调度实验系统,并应用于一类包含多颗成像卫星和多个地面站的全球目标观测应用,验证了所提出技术和方法的有效性。
Imaging from space is an important way to obtain the information about the earth's surface. Nowadays, the imaging applications in the field of social economy and military operation are featured by: broadly distributed imaging areas, frequent observing, various kinds of imaging satellites, instant obtaining of the imaging data, all of which lead to the rapid proliferation of earth observing demands. With the development of earth observing system composed of various imaging satellites, ground stations and data relay satellites, the control center as the core of the earth observing system needs to globally assign tasks to different imaging satellites and the corresponding receiving networks in order to image and receive image data, so that the earth observing needs can be maximally met. However, most of the current researches on earth observing scheduling are classified into two independent categories, i.e., satellites imaging scheduling and imaging data receiving scheduling. Thus there lacks a holistic method to integrate the two stages.
This dissertation proposes the integrated scheduling techniques for imaging satellites to holistically optimize both of the imaging process and data receiving process. In the dissertation, the integrated scheduling model is taken as the research foundation; scheduling optimization technologies through solution space transferring are taken as the research focus; moreover, relaxation methods are studied to validate the effectiveness of the proposed methods. The main work and contributions of this dissertation can be concluded as the following four parts:
1. Building the integrated scheduling model that comprises various kinds of resources.
The integrated scheduling model is the foundation of the whole research. Upon the thorough analysis on imaging satellites and the receiving network, the factors and their relations that should be considered in modeling the problem are summarized. An integrated scheduling model that comprises both the imaging process and receiving process is presented. Meanwhile, the proposed model is adaptive to changes of scheduling period.
2. Putting forward solution transfer method by permutation based representation for the integrated scheduling.
The integrated scheduling problem, which is non-linear and complicated, belongs to constraints optimization problems. Therefore, it is difficult to construct and optimize the feasible schedules through directly searching in the space of schedules. Thus, a solution space transfer method by permutation based representations is proposed. Through assigning resources to the permutations, the method transfer the searching of feasible scheduling from the space of schedules to the non-constraint space of permutations.
3. Presenting two kinds of optimization algorithms in the space of permutations to obtain optimal schedules.
Optimal schedules can be obtained by searching in the space of permutations for individuals with maximum fitness value. Characteristics of the swap neighborhood and insertion neighborhood in the space of permutations are analyzed based on neighborhood graph and symmetric group theories. Then the dissertation puts forward a stochastic neighborhood search algorithm with memory, which has simple structure and converges very fast. To obtain more optimized schedules, a hybrid genetic search algorithm is proposed. The algorithm can keep its global astringency; what's more, it strengthens its local optimizing ability through neighborhood searching.
4. Presenting Lagrange relaxation method to estimate the optimized integrated scheduling.
The Integrated scheduling problem belongs to NP-Hard combinatorial optimization problems. The search algorithms can only approach the optimize schedules at limited computing costs. Thus, methods are needed to evaluate the optimal schedules. The dissertation proposes Lagrange relaxation method to solve this problem. A longest path search algorithm with polynomial complexity and a subgradient optimization algorithm are presented to solve the relaxation problem; therefore, the tight upper bound for the original problem can be gained. The experimental results show that schedule from the search algorithms in the space of permutations is very close to the upper bound, which proves that our proposed optimization algorithms are effective and can meet the needs of optimizing the integrated scheduling.
Based on the above achievements, the thesis designs and implements an experimental system, which has been used to solve a worldwide earth observation proble through multiple imaging satellites and receiving groundstations. This system validates the efficiency and practicability of the presented techiques.
论文提出了星地资源综合调度的思想来解决卫星成像过程和数据接收过程的整体优化问题,以星地综合调度模型为基础,重点研究了基于置换表示的调度优化技术,并通过松弛优化方法验证了所提方法的有效性。主要工作和创新点包括:
1、建立了包含多类型星地资源的星地综合调度数学模型。
星地综合调度模型是星地综合调度研究的基础。论文分析了成像卫星和接收资源的工作特性,研究了星地综合调度问题的组成要素及其相互关系,提出了将成像活动和接收活动进行组合的星地综合调度数学模型,并可适应调度周期的变化。
2、提出了基于置换表示的星地综合调度问题可行解表示方法。
星地综合调度问题是非线性复杂约束优化问题,直接在问题空间上进行搜索时可行调度的构造和优化难以实现。为此,论文研究了基于置换表示的可行解表示方法,通过为置换序列分配星地资源,将可行调度优化转化为无约束空间上的搜索问题。
3、研究了两类可行调度优化算法。
最优调度的搜索由在置换空间上搜索具有最大评价值置换序列的过程实现。论文基于邻域图和对称群理论,分析了置换空间上交换邻域和插入邻域的性质,提出了具有简单结构的有记忆随机邻域搜索算法,可适应对调度优化时间的需要。为了获得更优化的调度,论文提出了一种混合遗传算法,在保留全局收敛性的同时,以邻域搜索算法增强了遗传算法的局部寻优能力。
4、研究了基于拉格朗日松弛的星地综合调度可行解优化评价方法。
星地综合调度是一类NP难解的组合最优化问题,优化算法只能在可接受花费下给出该问题的近似最优解,但所得可行解的优化程度需要其它优化方法来评价。论文基于具有多项式复杂度的最长路径算法和次梯度优化算法求解星地综合调度松弛问题,以获得原问题的紧致上界。实验结果显示,基于置换表示方式的可行解优化算法,所得可行解非常接近松弛方法所得上界,证明了论文提出的星地综合调度优化方法具有较高的优化能力,可以满足星地综合调度问题的优化需求。
基于以上研究成果,论文最后设计并实现了星地综合调度实验系统,并应用于一类包含多颗成像卫星和多个地面站的全球目标观测应用,验证了所提出技术和方法的有效性。
Imaging from space is an important way to obtain the information about the earth's surface. Nowadays, the imaging applications in the field of social economy and military operation are featured by: broadly distributed imaging areas, frequent observing, various kinds of imaging satellites, instant obtaining of the imaging data, all of which lead to the rapid proliferation of earth observing demands. With the development of earth observing system composed of various imaging satellites, ground stations and data relay satellites, the control center as the core of the earth observing system needs to globally assign tasks to different imaging satellites and the corresponding receiving networks in order to image and receive image data, so that the earth observing needs can be maximally met. However, most of the current researches on earth observing scheduling are classified into two independent categories, i.e., satellites imaging scheduling and imaging data receiving scheduling. Thus there lacks a holistic method to integrate the two stages.
This dissertation proposes the integrated scheduling techniques for imaging satellites to holistically optimize both of the imaging process and data receiving process. In the dissertation, the integrated scheduling model is taken as the research foundation; scheduling optimization technologies through solution space transferring are taken as the research focus; moreover, relaxation methods are studied to validate the effectiveness of the proposed methods. The main work and contributions of this dissertation can be concluded as the following four parts:
1. Building the integrated scheduling model that comprises various kinds of resources.
The integrated scheduling model is the foundation of the whole research. Upon the thorough analysis on imaging satellites and the receiving network, the factors and their relations that should be considered in modeling the problem are summarized. An integrated scheduling model that comprises both the imaging process and receiving process is presented. Meanwhile, the proposed model is adaptive to changes of scheduling period.
2. Putting forward solution transfer method by permutation based representation for the integrated scheduling.
The integrated scheduling problem, which is non-linear and complicated, belongs to constraints optimization problems. Therefore, it is difficult to construct and optimize the feasible schedules through directly searching in the space of schedules. Thus, a solution space transfer method by permutation based representations is proposed. Through assigning resources to the permutations, the method transfer the searching of feasible scheduling from the space of schedules to the non-constraint space of permutations.
3. Presenting two kinds of optimization algorithms in the space of permutations to obtain optimal schedules.
Optimal schedules can be obtained by searching in the space of permutations for individuals with maximum fitness value. Characteristics of the swap neighborhood and insertion neighborhood in the space of permutations are analyzed based on neighborhood graph and symmetric group theories. Then the dissertation puts forward a stochastic neighborhood search algorithm with memory, which has simple structure and converges very fast. To obtain more optimized schedules, a hybrid genetic search algorithm is proposed. The algorithm can keep its global astringency; what's more, it strengthens its local optimizing ability through neighborhood searching.
4. Presenting Lagrange relaxation method to estimate the optimized integrated scheduling.
The Integrated scheduling problem belongs to NP-Hard combinatorial optimization problems. The search algorithms can only approach the optimize schedules at limited computing costs. Thus, methods are needed to evaluate the optimal schedules. The dissertation proposes Lagrange relaxation method to solve this problem. A longest path search algorithm with polynomial complexity and a subgradient optimization algorithm are presented to solve the relaxation problem; therefore, the tight upper bound for the original problem can be gained. The experimental results show that schedule from the search algorithms in the space of permutations is very close to the upper bound, which proves that our proposed optimization algorithms are effective and can meet the needs of optimizing the integrated scheduling.
Based on the above achievements, the thesis designs and implements an experimental system, which has been used to solve a worldwide earth observation proble through multiple imaging satellites and receiving groundstations. This system validates the efficiency and practicability of the presented techiques.
引文
[1]王永刚,刘玉文.军事卫星及应用概论[M].北京:国防工业出版社, 2003.
[2]张钧屏,方艾里,万志龙.对地观测与对空监视[M].北京:科学出版社, 2001.
[3]岳涛,黄宇民,刘品雄,等.未来中国卫星遥感器的发展分析[J].航天器工程, 2008, 17(4): 77-82.
[4]中国人民解放军总参谋部军训部.外军高技术武器装备介绍[M].北京:解放军出版社,1997.
[5]冯贵年,于志坚.跟踪与数据中继卫星系统的现状和发展[J].中国航天, 2004, No.1: 16-19.
[6] European Space Agency, World Wide Tracking Station Network [EB/OL]. http://www.esa.int/spacecraftops/ESOC-Article-fullArticle_item_selected-20_10_00_par-42_1084173608409.html.
[7]我国十一五将建60至70颗卫星组成的空间信息系统[EB/OL]. Http:// www.gov.cn, 2007年05月16日.
[8]国防科学技术工业委员会.航天发展“十一五”规划[R/OL]. http:// www.cnsa.gov.cn/ n615708/ n620168/ n620180/ appendix/ 20071018172415.doc, 2007年8月.
[9] Myron H, Mcleod G, Mesic R, et al. Enhancing Dynamic Command and Control of Air Operations Against Time Critical Targets [M]. Santa Monica: RAND Corp., 2002.
[10] Hall N G, Magazine M J. Maximizing the Value of a Space Mission [J]. European Journal of Operation Research, 1994, 78: 224-241.
[11] Pemberton J, Galiber F. A Constraint–Based Approach to Satellite Scheduling [J]. DIMACS Series in Discrete Mathematics and Theoretical Computer Science: Constraint Programming and Large Scale Discrete Optimization, 2001, 57: 101-114.
[12] Pemberton J. Towards Scheduling Over–Constrained Remote Sensing Satellites [C]// Proc. of the 2nd NASA International Workshop on Planning and Scheduling for Space. [s.n.], 2000: 84-89.
[13] Agnese J C, Bataille N, Blulmstein D, et al. Exact and approximate methods for the daily management of an earth observation satellite [C]// Proc. of the 5th ESA Workshop on Artificial Intelligence and Knowledge Based Systems for Space. Paris: ESA, 1995
[14] Bensana E, Verfaillie G, Agnese J C, et al. Exact and Inexact Methods for the Daily Management of an Earth Observing Satellite [C]// Proc. of the 4th Int. Symposium on Space Mission Operations and Ground Data Systems. Paris: ESA, 1996.
[15] Verfaillie G, Lemaitre M, Schiex T. Russian Doll Search for Solving Constraint OptimizationProblems [C]// 8th innovative Applications of Artificial Intelligence Conerence: Proc. of the 13th National Conference on Artificial Intelligence. AAAI, 1996, Vol.1:181-187.
[16] Gabrel V. Improved Linear Programming Bounds via Column Generation for Daily Scheduling of Earth Observation Satellite [R]. Research Report 99-01, LIPN, Paris XIII University, Jan. 1999.
[17] Gabrel V, Vanderpooten D. Enumeration and Interactive Selection of Efficient Paths in a Multiple Criteria Graph for Scheduling an Earth Observing Satellite [J]. European Journal of Operational Research. 2002, 139: 533-542.
[18] Gabrel V, Murat C. Mathematical Programming for Earth Observation Satellite Mission Planning [M]. Ciriani T, Fasano G, Gliozzi S, Tadei R. Operations Research in Space and Air. 2003: 103-122.
[19] Gabrel V. An Extensive Comparison of 0–1 Linear Programs for SPOT5 Daily Mission Planning Problem [J]. Annales du LAMSADE, 2005, no. 4-5: 283-293.
[20] Gabrel V. Strengthened 0-1 Linear Formulation for the Daily Satellite Mission Planning [J]. J Comb Optim, 2006, 11: 341-346.
[21] Vasquez M, Hao J K. A“Logic-Constrained”Knapsack Formulation and a Tabu Algorithm for the Daily Photograph Scheduling of an Earth Observation Satellite [J]. Computational Optimization and Applications. 2001, 20(2): 137-157
[22] Vasquez M, Hao J K. Upper Bounds for the SPOT 5 Daily Photograph Scheduling Problem [J]. Journal of Combinatorial Optimization, 2003,7: 87-103.
[23] Lemaitre M, Verfaillie G. Daily management of an earth observation satellite: Comparison of ILOG Solver with dedicated algorithms for Valued Constraint Satisfaction Problems [C]// 3rd ILOG International Users Meeting. 1997.
[24] Schiex T, Fargier H, Verfaillie G. Valued Constraint Satisfaction Problems: Hard and Easy Problems [C]// Proc. of the 14th International Joint Conference on Artificial Intelligence. Morgan Kaufmann, 1995, Vol.1: 631-639.
[25] Bensana E, Verfaillie G, Lemaitre M. Earth Observing Satellite Management [J]. Constraints, 1999, 4(3):293-299.
[26] Potter W. A Photo Album of Earth: Scheduling Landsat 7 Mission Daily Activities [C]. Proc. of the 5th International Symposium on Space Mission Operations and Ground Data Systems. [s.n.], 1998.
[27] Yamaguchi Y, Kawakami T, Kahle A B, et al. ASTER Mission Planning and Operations Concept [C]. Proc. of the 5th International Symposium on Space Mission Operations and Ground Data Systems. [s.n.], 1998.
[28] Muraoka H, Cohen R H, Ohno T, Doi N. ASTER Observing Scheduling Algorithms [C].Proc. of the 5th International Symposium on Space Mission Operations and Ground Data Systems. [s.n.], 1998.
[29] Wolfe W J, Sorensen S E. Three Scheduling Algorithms Applied to the Earth Observing Systems Domain [J]. Management Science, 2000(46):148–168.
[30] Wu A M, Lee Y Y, Kuo P, et al. Rocsat-2 Imaging Planning and Scheduling [C]// 1st Taipei International Conference on Digital Earth. [s.n.], 2003.
[31] Lin W C, Liao D Y. Satellite Imaging Scheduling with Tabu Search vs. Lagrangian Relaxation Approaches [C]//第一届台湾作业研究学会学术研讨会暨2004年科技与管理学术研讨会. [s.n.], 2004.
[32] Lin W C, Liao D Y. A Tabu Search Algorithm for Satellite Imaging Scheduling [C]// Proc. of IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 2004: 1886-1891.
[33] Lin W C, Liao D Y, Liu C Y, et al. Daily Imaging Scheduling of An Earth Observation Satellite [J]. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 2005, 35(2): 213-223.
[34]林伟诚.卫星取像排程之研究[D].台湾:国立台湾大学, 2005.
[35]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[D].长沙:国防科技大学, 2005.
[36] Knight R, Smith B. Optimally Solving Nadir Observation Scheduling Problems [C]// Proc. of ISAIRAS Conference. [s.n.], 2005.
[37] Lee S, Jung W C, Kim J H. Task Scheduling Algorithm for the Communication, Ocean, and Meteorological Satellite [J]. ETRI Journal, 2008, 30(1): 1-12.
[38] Hilland J E, Wessen R R, Porter D, et al. A Market-Based Conflict Resolution Approach for Satellite Mission Planning [J]. IEEE Transactions on Engineering Management, 2001, 48(3): 272-282.
[39] Harrison S A, Price M E. Task scheduling for satellite based imagery [C]. Proc. of the 18th Workshop of the UK Planning and Scheduling Special Interest Group. [s.n.], 1999.
[40] Gabrel V, Moulet A, Murat C, et al. A New Single Model and Derived Algorithms for the Satellite Shot Planning Problem Using Graph Theory Concepts [J]. Annals of Operations Research, 1997, 69: 115-134.
[41] Verfaillie G, Lemaitre M. Selecting and Scheduling Observations for Agile Satellites: Some Lessons from the Constraint Reasoning Community Point of View [C] // LNCS 2239: Proc. of 7th International Conference on Practical and Principles of Constraint Programming. Berlin: Springer, 2001: 670-684.
[42] Verfaillie G, Lemaitre G, Bataille N, et al. Management of the Mission of Earth Observation Satellites-Challege Description [R]. Technical Report, 2002.
[43] Verfaillie G, Lemaitre G, Bataille N, et al. Management of the Mission of Earth Observation Satellites-Informal Description of the Global Problem [R]. Technical Report, 2002.
[44] Lemaitre M, Verfaillie G, Jouhaud F. How to Manage the New Generation of Agile Earth Observation Satellites [C]// Proc. of the 6th International Symposium on Space Operations. [s.n.], 2000: 37-44.
[45] Lemaitre M, Verfaillie G, Jouhaud F, et al. Selecting and Scheduling Observations of Agile Satellites [J]. Aerospace Science and Technology. 2002, 6: 367-381.
[46] Mancel C. A Column Generation Approach for Earth Observing Satellites [C]// Proc. of the 2nd Operational Research Peripatetic Postgraduate Programme. [s.n.], 2003.
[47] Benoist T, Rottembourg B. Upper Bounds for Revenue Maximization in a Satellite Scheduling Problem [J]. Quarterly Journal of the Belgian, French and Italian Operations Research Societies, 2004, 2: 235-249.
[48] Habet D, Vasquez M. Saturated and Consistent Neighborhood for Selecting and Scheduling Photographs of Agile Earth Observing Satellite [C]// Proc. of the 5th Metaheuristics International Conference. [s.n.], 2003.
[49] Habet D, Vasquez M. Solving the Selecting and Scheduling Satellite Photographs Problem with a Consistent Neighborhood Heuristic [C]// Proc. of the 16th IEEE International Conference on Tools with Artificial Intelligence. IEEE Conputer Society, 2004: 302-309.
[50] Frank J, Jonsson A, Morris R, et al. Planning and Scheduling for Fleets of Earth Observing Satellites [C]// Proc. of the 6th International Symposium on Artificial Intelligence, Robotics, Automation and Space. [s.n.], 2002.
[51] Dungan, J, Frank, J, Jonsson, A, Morris. Advances in Planning and Scheduling of Remote Sensing Instruments for Fleets of Earth Orbiting Satellites [C]// Proc. of the 2nd Earth Science Technology Conference. [s.n.], 2002.
[52]李菊芳.航天侦察多星多地面站任务规划问题研究[D].长沙:国防科技大学, 2005.
[53] Bianchessi N, Piuri V, Righini G, et al. An Optimization Approach to the Planning of Earth Observing Satellites [C]// Proc. of the 4th International Workshop on Planning and Scheduling for Space. [s.n.], 2004.
[54] Bianchessi N, Cordeau J F, Desrosiers J, et al. A Heuristic for the Management of Multiple-Orbit Earth Observation Satellites [R]. Technical Report GERAD-2005-45, 2005.
[55] Bianchessi N. Planning and Scheduling Problems for Earth Observation Satellites:Models and Algorithms [D]. Milano, Italy: Universit`a degli studi di Milano, 2006.
[56] Bianchessi N, Cordeau J F, Desrosiers J, et al. A Heuristic for the Multi-Satellite, Multi-Orbit and Multi-User Management of Earth Observation Satellites [J]. European Journal of Operational Research, 2007, 177: 750-762.
[57] Bianchessi N, Righini G. A Mathematical Programming Algorithm for Planning and Scheduling an Earth Observing SAR Constellation [C]// Proc. of 5th International Workshop on Planning and Scheduling for Space. [s.n.], 2006.
[58] Bianchessi N, Righini G. Planning and Scheduling Algorithms for the COSMO-SkyMed Constellation [J]. Aerospace science and technology, 2008, 12(7): 535-544.
[59]贺仁杰.成像侦察卫星调度问题研究[D].长沙:国防科技大学, 2004.
[60] Globus A, Crawford J, Lohn J, et al. Scheduling Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach [C]. Proc. of the 3rd NASA International Workshop on Planning and Scheduling for Space. [s.n.], 2002.
[61] Globus A, Crawford J, Lohn J, et al. Scheduling Earth Observing Satellites with Evolutionary Algorithms [C]. Proc. of the 1st International Conference on Space Mission Chalenges for 17 Information Technologies. [s.n.], 2003.
[62] Globus A, Crawford J, Lohn J. A Comparison of Techniques for Scheduling Earth Observing Satellites [C]. 8th innovative Applications of Artificial Intelligence Conerence: Proc. of the 16th Conference on Innovative Applications of Artificial Intelligence. AAAI, 2004: 836–843.
[63] Bonissone P P, Subbu R, Eklund N, et al. Evolutionary Algorithms + Domain Knowledge = Real-World Evolutionary Computation [J]. IEEE Transactions on Evolutionary Computation, 2006, 10(3): 256-280.
[64]王钧.成像卫星综合任务调度模型与优化方法研究[D].长沙:国防科技大学, 2007.
[65] Florio S D, Zehetbauer T, Neff T. Optimal Operations Planning for SAR Satellite Constellations In Low Earth Orbit [C]// Proc. of the 6th International Symposium on Reducing the Costs of Spacecraft Ground Systems and Operations. ESA, 2005.
[66] Florio S D. Performances Optimization of Remote Sensing Satellite Constellations: a Heuristic Method [C]// Proc. of the International Workshop on Planning and Scheduling for Space. [s.n.], 2006.
[67] Gooley T D. Automating the Satellite Range Scheduling Process [D]. OH: Air Force Institute of Technology, 1993.
[68] Schalck S M. Automating Satellite Range Scheduling [D]. OH: Air Force Institute of Technology, 1993.
[69] Colin E B. Scheduling Deep-space Network Data Transmissions: a Lagrangian Relaxation Approach [C]// Proc. SPIE, 1993, Vol. 1963: 330-340.
[70] Parish D A. A Genetic Algorithm Approach to Automating Satellite Range Scheduling [D]. Air Force Institute of Technology, USA, 1994.
[71] Jang K. The Capacity of The Air Force Satellite Control Network [D]. OH: Air Force Institute of Technology, USA, 1996.
[72] Burrowbridge S E. Optimal Allocation of Satellite Networks Resource [D]. VA: Virginia Tech. University, 1999.
[73] Howe A E, Whitley L D, Watson J P, Barbulescu L. A Study of Air Force Satellite Access Scheduling [C]. Proc. of the World Automation Congress. [s.n.], 2000.
[74] Howe A E, Whitley L D, Barbulescu L, Watson J P. Mixed Initiative Scheduling for the Air Force Satellite Control Network [C]. Proc. of the 2nd International NASA Workshop on Planning and Scheduling for Space. [s.n.], 2000.
[75] Barbulescu L, Howe A E, Watson J P, et al. Satellite Range Scheduling: A Comparison of Genetic, Heuristic and Local Search [C]// LNCS 2439: Proc. of the 7th International Conference on Parallel Problem Solving from Nature. Springer, 2002.
[76] Barbulescu L,Watson J P,Whitley L D, Howe A E. Scheduling Space–Ground Communications for the Air Force Satellite Control Network [J]. Journal of Scheduling, 2004, 7(1): 1094-6136.
[77] Barbulescu L, Howe A E, Whitley L D, Roberts M. Trading places: How to Schedule More in a Multi-resource Oversubscribed Scheduling Problem [C]// Proc. of the International Conference on Planning and Scheduling. AAAI, 2004.
[78] Barbulescu L, Howe A E, Whitley L D, et al. AFSCN Scheduling: How the Problem and Solution Have Evolved [J]. Mathematical and Computer Modeling, 2006, 43(9): 1023-1037.
[79] Barbulescu L, Whitley L D, Howe A E. Leap Before You Look: An Effective Strategy in an Oversubscribed Scheduling Problem [C]// Proc. of the 9th National Conference on Artifficial Intelligence. AAAI, 2004.
[80] Roberts M, Whitley L D,Howe A E, Barbulescu L. Random Walks and Neighborhood Bias in Oversubscribed Scheduling [C]// Proc. of the 2nd Multidisciplinary International Conference on Scheduling: Theory and Applications. [s.n.], 2005.
[81] Rao J D, Soma P, Padmashree G S. Multi-Satellite Scheduling System for Leo Satellite Operations [C]// Proc. of the 5th International Conference on Space Operations. [s.n.], 1998.
[82] Soma P, Venkateswarlu S, Santhalakshmi S, et al. Multi-Satellite scheduling using Genetic Algorithms [C]. Proc. of the 8th International Conference on Space Operations. [s.n.], 2004.
[83]王远振,赵坚,聂成.多卫星-地面站系统的Petri网模型研究[J].空军工程大学学报(自然科学版), 2003, 4(2):7-10.
[84]王远振,赵坚,聂成.多星地面站设备优化调度方法研究[J].计算机仿真, 2003, 20(7): 17-19.
[85]王远振,高卫斌,聂成.多星地面站系统资源配置优化研究综述[J].系统工程与电子技术, 2004, 26(4):437-439.
[86]李云峰.卫星-地面站接收调度模型及算法研究[D].长沙:国防科技大学, 2007.
[87] Mancel C, Lopez P. Complex optimization problems in space systems [C]// Proc. of American Association for Aritficial Intelligence. AAAI, 2003.
[88] Marinelli F, Nocella S, Rossi F, et al. A Lagrangian Heuristic for Satellite Range Scheduling with Resource Constraints [R]. Technical Report TRCS 004, Universit`a di L’Aquila, 2005.
[89] Oddi A, Cesta A, Policella N, et al. Scheduling Downlink Operations in MARS-EXPRESS [C]// Proc. of the 3rd NASA Workshop on Planning and Scheduling. [s.n.], 2002.
[90] Oddi A, Policella N, Cesta A, et al. Generating High Quality Schedules for a Spacecraft Memory Downlink Problem [C]// LNCS 2833: CP 2003, 9th International Conference. Berlin: Springer, 2003: 570-584.
[91]方炎申.中继卫星调度模型研究[D].长沙:国防科技大学, 2007.
[92]任萱.军事航天技术[M].北京:国防工业出版社, 1999.
[93] Nieke J, Schwartzer H, Neumann A, et al. Imaging Spaceborne and Airborne Sensor Systems in the Beginning of the Next Century [C]// Conference on Sensors, Systems and Next Generation Satellites -the European Symposium on Aerospace Remote Sensing. III. SPIE, 1997, Vol: 3221.
[94]孙佳.国外合成孔径雷达卫星发展趋势分析[J].装备指挥技术学院学报, 2007, 18(1): 67-70.
[95]魏钟铨.合成孔径雷达卫星[M].北京:科学出版社, 2001.
[96]詹辛农. 21世纪航天数据存储技术展望[J].中国航天, 1997, No.2: 34-37.
[97]朱岩.基于闪存的星载高速大容量存储技术的研究[D].北京:中国科学院空间科学与应用研究中心, 2006
[98]王永东,崔容强,徐秀琴.空间太阳电池发展现状及展望[J].电源技术, 2001, 25(5): 182-185.
[99]鄢婉娟.太阳同步轨道卫星电源系统设计计算方法研究[J].中国空间科学技术, 2001, No.2: 19-25.
[100]中国首颗中继卫星“天链一号”01星发射成功[N].中国空间科学技术, 2008, No.6: 14.
[101]曾德贤,董绪荣,李睿. LEO卫星接入TDRSS的可行性分析与仿真[J].电子与信息学报, 2008, 30(4): 772-775.
[102]吕海寰,蔡剑铭,甘仲民.卫星通信系统[M].北京:中国邮电出版社, 1994.
[103]刘嘉兴.近空问跟踪与数据中继系统的初步设想[J].电讯技术, 2008, 48(5): 46-50.
[104] Verfaillie G, Lemaitre M, Bataille N, Lachiver J M. Management of the Mission of Earth Observation Satellites, Challenge Description [R]. Technical report, 2002.
[105] Garey M R, Johnson D S. Computers and Intractability: A Guide to the Theory of NP-Completeness [M]. W. H. Freeman and Company, 1979.
[106] Holland J H. Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press, 1975.
[107] Kramer L, Barbulescu L, Smith S. Analyzing Basic Representa-tion Choices in Oversubscribed Scheduling Problems [C]// Proc. of the 3rd Multidisciplinary International Conference on Scheduling: Theory and Application. [s.n.], 2007.
[108] Back T, Fogel D B, Michalewicz Z. Handbook of Evolutionary Computation [M], IOP Publishing Ltd and Oxford University Press, 1997.
[109] Goldberg D E. Genetic Algorithms in Search, Optimizaiton and Machine Learning [M]. MA: Addison-Wesley, 1989.
[110] Knuth D E. The Art of Computer Programming [M], Vol. 3, Second Edition. Addison Wesley, 1998.
[111] Coello C. Theoretical and Numerical Constraint-Handling Techniques Used with Evolutionary Algorithms: a Survey of the State of the Art [J]. Computer Methods in Applied Mechanics and Engineering, 2002, 191 (11/12): 1245-1287.
[112] Michalewicz Z, Dasgupta D, Le R, et al. Evolutionary Algorithms for Constrained Engineering Problems [J]. Computers & Industrial Engineering, 1996, 30(4): 851-970.
[113] Michalewicz Z, Schoenauer M. Evolutinary Computation for Constrained Parameter Optimization Problems [J]. Evolutionary Computation, 1996, 4(1): 1-32.
[114]余文,李文厚.遗传算法对约束优化问题的研究综述[J].计算机科学, 2002, 29(6): 98-101.
[115]王勇,蔡自兴,周育人,肖赤心.约束优化进化算法研究及其进展[J].软件学报, 2007, 18(11): 2691?2706.
[116] Campos V, Laguna M, Marti R. Context-Independent Scatter and Tabu Search for Permutation Problems [J]. INFORMS Journal on Computing, 2005, 17(1): 111-122.
[117] Bierwirth C, Mattfeld D C, Kopter Herbert. On Permutation Representations for Scheduling Problems [C]// LNCS 1141: Proc. of the 4th International Conference on Parallel Problem Solving from Nature. Berlin: Springer, 1996: 310-318.
[118]刑文训,谢金星.现代优化计算方法[M].北京:清华大学出版社, 2006.
[119] Whitley D, Watson J P. Complexity Theory and the No Free Lunch Theorem [M]. Search Methodologies: Introducting Tutorials in Optimization and Decision Support Thchnologies, Edited by E K Burke and G Kendall, Springer, 2006.
[120]王凌,郑大钟. Meta-heuristic算法研究进展[J].控制与决策, 2000, 15(3): 257-262.
[121] Christian Blum, Andrea Roli. Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison [J]. ACM Computing Surveys, 2003, 35(3): 268-308.
[122] Michel Gendreau, Jean-Yves Potvin. Metaheuristics in Combinatorial Optimization [J].Annals of Operations Research, 2005, 140: 189-213.
[123] Mitra D, Romeo F, Vincentelli A S. Convergence and Finite Time Behavior of Simulated Annealing [J]. Adv. Appl. Prob., 1986, 18: 747-771.
[124] Hajek B. Cooling Schedules for Optimal Annealing [J]. Math OperRes, 1988, 13(2): 311-329.
[125] Eiben A E, Aarts E H, Van Hee K M. Global Convergence of Genetic Algorithms: An Infinite Markov Chain Analysis [J]. Parallel Problem Solving from Nature. Berlin: Springer-Verlag, 1991: 4-12.
[126] Rudolph G. Convergence Properties of Canonical Genetic Algorithms [J]. IEEE Trans. on Neural Network, 1994, 5(1): 96-10.
[127]李超,谢端强,冯良贵,等.代数学基础[M].长沙:国防科技大学出版社, 2000.
[128] Mitchell M, Holland J H, Forrest S. When Will a Genetic Algorithm Outperform Hill Climbing [J]. Advances in Neural Information Processing Systems 6, 1993: 51-58
[129] Juels A, Wattenberg M. Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms [R]. Technical Report, CSD-94-834, Department of Computer Science, University of California at Berkeley, 1994.
[130] Kirkpatrick S, Gelatt C D, Vecchi M P. Optimization by Simulated Annealing [J]. Science, 1983, 220(4598): 671-680.
[131]玄光男,程润伟.遗传算法与工程设计[M].北京:科学出版社, 2000.
[132] Goldberg D, Lingle R. Alleles, loci, and the Traveling Salesman Problem [C]// Grefenstette J J. Proc. of International Conference on Genetic Algorithms and their Applications. Hillsdale, NJ: Lawrence Erlbaum Associates, 1985: 154-159.
[133] Davis L. Applying Adaptive Algorithms to Epistatic Domains [C]// IJCAI. Proc. of International Joint Conference on Artificial Intelligence. Morgan Kaufmann, 1985.
[134] Syswerda G. Schedule Optimization Using Genetic Algorithms [M]// Davis L. Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold, 1991: 332-349.
[135] Oliver I, Smith D, Holland J. A Study of Permutation Crossover Operators on the Traveling Salesman Problems [C]// Grefenstette J J. Proc. of 2nd International Conference on Genetic Algorithms and their Applications. Hillsdale, NJ: Lawrence Erlbaum Associates, 1987: 224-230.
[136] Syswerda G. Uniform crossover in genetic algorithms [C]// Schaffer J D. Proc. of the 3rd International Conference on Genetic Algorithms. San Mateo, CA, USA: Morgan Kaufmann, 1989: 2-9.
[137] Whitley D. The GENITOR Algorithm and Selective Pressure: Why Rank-Based Allocation of Reproductive Trials is Best [C]// Schaffer J D. Proc. of the 3rd InternationalConference on Genetic Algorithms. San Mateo, CA, USA: Morgan Kaufmann, 1989: 116-121.
[138] Glover F. Surrogate Constraint Duality in Mathematical Programming [J]. Operations Research, 1975, 23(3): 434-451.
[139] Geoffrion A M, Lagrangian Relaxation for Integer Programming [J]. Mathematical Programming Study, 1974: 82-114.
[140] Fisher M L. The Lagrangian Relaxation Method for Solving Integer Programming Problems [J]. Management Science, 1981, 27(1): 1-18
[141] Arnaud F. The Multidimensional 0-1 Knapsack Problem: An Overview [J]. European Journal of Operational Research, 2004, 155: 1-12.
[142] Ruedee Rangsaritratsamee, William G. Ferrell Jr., Mary Beth Kurz. Dynamic Rescheduling That Simultaneously Considers Efficiency and Stability [J]. Computers & Industrial Engineering, 2004, 46: 1-15.
[2]张钧屏,方艾里,万志龙.对地观测与对空监视[M].北京:科学出版社, 2001.
[3]岳涛,黄宇民,刘品雄,等.未来中国卫星遥感器的发展分析[J].航天器工程, 2008, 17(4): 77-82.
[4]中国人民解放军总参谋部军训部.外军高技术武器装备介绍[M].北京:解放军出版社,1997.
[5]冯贵年,于志坚.跟踪与数据中继卫星系统的现状和发展[J].中国航天, 2004, No.1: 16-19.
[6] European Space Agency, World Wide Tracking Station Network [EB/OL]. http://www.esa.int/spacecraftops/ESOC-Article-fullArticle_item_selected-20_10_00_par-42_1084173608409.html.
[7]我国十一五将建60至70颗卫星组成的空间信息系统[EB/OL]. Http:// www.gov.cn, 2007年05月16日.
[8]国防科学技术工业委员会.航天发展“十一五”规划[R/OL]. http:// www.cnsa.gov.cn/ n615708/ n620168/ n620180/ appendix/ 20071018172415.doc, 2007年8月.
[9] Myron H, Mcleod G, Mesic R, et al. Enhancing Dynamic Command and Control of Air Operations Against Time Critical Targets [M]. Santa Monica: RAND Corp., 2002.
[10] Hall N G, Magazine M J. Maximizing the Value of a Space Mission [J]. European Journal of Operation Research, 1994, 78: 224-241.
[11] Pemberton J, Galiber F. A Constraint–Based Approach to Satellite Scheduling [J]. DIMACS Series in Discrete Mathematics and Theoretical Computer Science: Constraint Programming and Large Scale Discrete Optimization, 2001, 57: 101-114.
[12] Pemberton J. Towards Scheduling Over–Constrained Remote Sensing Satellites [C]// Proc. of the 2nd NASA International Workshop on Planning and Scheduling for Space. [s.n.], 2000: 84-89.
[13] Agnese J C, Bataille N, Blulmstein D, et al. Exact and approximate methods for the daily management of an earth observation satellite [C]// Proc. of the 5th ESA Workshop on Artificial Intelligence and Knowledge Based Systems for Space. Paris: ESA, 1995
[14] Bensana E, Verfaillie G, Agnese J C, et al. Exact and Inexact Methods for the Daily Management of an Earth Observing Satellite [C]// Proc. of the 4th Int. Symposium on Space Mission Operations and Ground Data Systems. Paris: ESA, 1996.
[15] Verfaillie G, Lemaitre M, Schiex T. Russian Doll Search for Solving Constraint OptimizationProblems [C]// 8th innovative Applications of Artificial Intelligence Conerence: Proc. of the 13th National Conference on Artificial Intelligence. AAAI, 1996, Vol.1:181-187.
[16] Gabrel V. Improved Linear Programming Bounds via Column Generation for Daily Scheduling of Earth Observation Satellite [R]. Research Report 99-01, LIPN, Paris XIII University, Jan. 1999.
[17] Gabrel V, Vanderpooten D. Enumeration and Interactive Selection of Efficient Paths in a Multiple Criteria Graph for Scheduling an Earth Observing Satellite [J]. European Journal of Operational Research. 2002, 139: 533-542.
[18] Gabrel V, Murat C. Mathematical Programming for Earth Observation Satellite Mission Planning [M]. Ciriani T, Fasano G, Gliozzi S, Tadei R. Operations Research in Space and Air. 2003: 103-122.
[19] Gabrel V. An Extensive Comparison of 0–1 Linear Programs for SPOT5 Daily Mission Planning Problem [J]. Annales du LAMSADE, 2005, no. 4-5: 283-293.
[20] Gabrel V. Strengthened 0-1 Linear Formulation for the Daily Satellite Mission Planning [J]. J Comb Optim, 2006, 11: 341-346.
[21] Vasquez M, Hao J K. A“Logic-Constrained”Knapsack Formulation and a Tabu Algorithm for the Daily Photograph Scheduling of an Earth Observation Satellite [J]. Computational Optimization and Applications. 2001, 20(2): 137-157
[22] Vasquez M, Hao J K. Upper Bounds for the SPOT 5 Daily Photograph Scheduling Problem [J]. Journal of Combinatorial Optimization, 2003,7: 87-103.
[23] Lemaitre M, Verfaillie G. Daily management of an earth observation satellite: Comparison of ILOG Solver with dedicated algorithms for Valued Constraint Satisfaction Problems [C]// 3rd ILOG International Users Meeting. 1997.
[24] Schiex T, Fargier H, Verfaillie G. Valued Constraint Satisfaction Problems: Hard and Easy Problems [C]// Proc. of the 14th International Joint Conference on Artificial Intelligence. Morgan Kaufmann, 1995, Vol.1: 631-639.
[25] Bensana E, Verfaillie G, Lemaitre M. Earth Observing Satellite Management [J]. Constraints, 1999, 4(3):293-299.
[26] Potter W. A Photo Album of Earth: Scheduling Landsat 7 Mission Daily Activities [C]. Proc. of the 5th International Symposium on Space Mission Operations and Ground Data Systems. [s.n.], 1998.
[27] Yamaguchi Y, Kawakami T, Kahle A B, et al. ASTER Mission Planning and Operations Concept [C]. Proc. of the 5th International Symposium on Space Mission Operations and Ground Data Systems. [s.n.], 1998.
[28] Muraoka H, Cohen R H, Ohno T, Doi N. ASTER Observing Scheduling Algorithms [C].Proc. of the 5th International Symposium on Space Mission Operations and Ground Data Systems. [s.n.], 1998.
[29] Wolfe W J, Sorensen S E. Three Scheduling Algorithms Applied to the Earth Observing Systems Domain [J]. Management Science, 2000(46):148–168.
[30] Wu A M, Lee Y Y, Kuo P, et al. Rocsat-2 Imaging Planning and Scheduling [C]// 1st Taipei International Conference on Digital Earth. [s.n.], 2003.
[31] Lin W C, Liao D Y. Satellite Imaging Scheduling with Tabu Search vs. Lagrangian Relaxation Approaches [C]//第一届台湾作业研究学会学术研讨会暨2004年科技与管理学术研讨会. [s.n.], 2004.
[32] Lin W C, Liao D Y. A Tabu Search Algorithm for Satellite Imaging Scheduling [C]// Proc. of IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 2004: 1886-1891.
[33] Lin W C, Liao D Y, Liu C Y, et al. Daily Imaging Scheduling of An Earth Observation Satellite [J]. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 2005, 35(2): 213-223.
[34]林伟诚.卫星取像排程之研究[D].台湾:国立台湾大学, 2005.
[35]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[D].长沙:国防科技大学, 2005.
[36] Knight R, Smith B. Optimally Solving Nadir Observation Scheduling Problems [C]// Proc. of ISAIRAS Conference. [s.n.], 2005.
[37] Lee S, Jung W C, Kim J H. Task Scheduling Algorithm for the Communication, Ocean, and Meteorological Satellite [J]. ETRI Journal, 2008, 30(1): 1-12.
[38] Hilland J E, Wessen R R, Porter D, et al. A Market-Based Conflict Resolution Approach for Satellite Mission Planning [J]. IEEE Transactions on Engineering Management, 2001, 48(3): 272-282.
[39] Harrison S A, Price M E. Task scheduling for satellite based imagery [C]. Proc. of the 18th Workshop of the UK Planning and Scheduling Special Interest Group. [s.n.], 1999.
[40] Gabrel V, Moulet A, Murat C, et al. A New Single Model and Derived Algorithms for the Satellite Shot Planning Problem Using Graph Theory Concepts [J]. Annals of Operations Research, 1997, 69: 115-134.
[41] Verfaillie G, Lemaitre M. Selecting and Scheduling Observations for Agile Satellites: Some Lessons from the Constraint Reasoning Community Point of View [C] // LNCS 2239: Proc. of 7th International Conference on Practical and Principles of Constraint Programming. Berlin: Springer, 2001: 670-684.
[42] Verfaillie G, Lemaitre G, Bataille N, et al. Management of the Mission of Earth Observation Satellites-Challege Description [R]. Technical Report, 2002.
[43] Verfaillie G, Lemaitre G, Bataille N, et al. Management of the Mission of Earth Observation Satellites-Informal Description of the Global Problem [R]. Technical Report, 2002.
[44] Lemaitre M, Verfaillie G, Jouhaud F. How to Manage the New Generation of Agile Earth Observation Satellites [C]// Proc. of the 6th International Symposium on Space Operations. [s.n.], 2000: 37-44.
[45] Lemaitre M, Verfaillie G, Jouhaud F, et al. Selecting and Scheduling Observations of Agile Satellites [J]. Aerospace Science and Technology. 2002, 6: 367-381.
[46] Mancel C. A Column Generation Approach for Earth Observing Satellites [C]// Proc. of the 2nd Operational Research Peripatetic Postgraduate Programme. [s.n.], 2003.
[47] Benoist T, Rottembourg B. Upper Bounds for Revenue Maximization in a Satellite Scheduling Problem [J]. Quarterly Journal of the Belgian, French and Italian Operations Research Societies, 2004, 2: 235-249.
[48] Habet D, Vasquez M. Saturated and Consistent Neighborhood for Selecting and Scheduling Photographs of Agile Earth Observing Satellite [C]// Proc. of the 5th Metaheuristics International Conference. [s.n.], 2003.
[49] Habet D, Vasquez M. Solving the Selecting and Scheduling Satellite Photographs Problem with a Consistent Neighborhood Heuristic [C]// Proc. of the 16th IEEE International Conference on Tools with Artificial Intelligence. IEEE Conputer Society, 2004: 302-309.
[50] Frank J, Jonsson A, Morris R, et al. Planning and Scheduling for Fleets of Earth Observing Satellites [C]// Proc. of the 6th International Symposium on Artificial Intelligence, Robotics, Automation and Space. [s.n.], 2002.
[51] Dungan, J, Frank, J, Jonsson, A, Morris. Advances in Planning and Scheduling of Remote Sensing Instruments for Fleets of Earth Orbiting Satellites [C]// Proc. of the 2nd Earth Science Technology Conference. [s.n.], 2002.
[52]李菊芳.航天侦察多星多地面站任务规划问题研究[D].长沙:国防科技大学, 2005.
[53] Bianchessi N, Piuri V, Righini G, et al. An Optimization Approach to the Planning of Earth Observing Satellites [C]// Proc. of the 4th International Workshop on Planning and Scheduling for Space. [s.n.], 2004.
[54] Bianchessi N, Cordeau J F, Desrosiers J, et al. A Heuristic for the Management of Multiple-Orbit Earth Observation Satellites [R]. Technical Report GERAD-2005-45, 2005.
[55] Bianchessi N. Planning and Scheduling Problems for Earth Observation Satellites:Models and Algorithms [D]. Milano, Italy: Universit`a degli studi di Milano, 2006.
[56] Bianchessi N, Cordeau J F, Desrosiers J, et al. A Heuristic for the Multi-Satellite, Multi-Orbit and Multi-User Management of Earth Observation Satellites [J]. European Journal of Operational Research, 2007, 177: 750-762.
[57] Bianchessi N, Righini G. A Mathematical Programming Algorithm for Planning and Scheduling an Earth Observing SAR Constellation [C]// Proc. of 5th International Workshop on Planning and Scheduling for Space. [s.n.], 2006.
[58] Bianchessi N, Righini G. Planning and Scheduling Algorithms for the COSMO-SkyMed Constellation [J]. Aerospace science and technology, 2008, 12(7): 535-544.
[59]贺仁杰.成像侦察卫星调度问题研究[D].长沙:国防科技大学, 2004.
[60] Globus A, Crawford J, Lohn J, et al. Scheduling Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach [C]. Proc. of the 3rd NASA International Workshop on Planning and Scheduling for Space. [s.n.], 2002.
[61] Globus A, Crawford J, Lohn J, et al. Scheduling Earth Observing Satellites with Evolutionary Algorithms [C]. Proc. of the 1st International Conference on Space Mission Chalenges for 17 Information Technologies. [s.n.], 2003.
[62] Globus A, Crawford J, Lohn J. A Comparison of Techniques for Scheduling Earth Observing Satellites [C]. 8th innovative Applications of Artificial Intelligence Conerence: Proc. of the 16th Conference on Innovative Applications of Artificial Intelligence. AAAI, 2004: 836–843.
[63] Bonissone P P, Subbu R, Eklund N, et al. Evolutionary Algorithms + Domain Knowledge = Real-World Evolutionary Computation [J]. IEEE Transactions on Evolutionary Computation, 2006, 10(3): 256-280.
[64]王钧.成像卫星综合任务调度模型与优化方法研究[D].长沙:国防科技大学, 2007.
[65] Florio S D, Zehetbauer T, Neff T. Optimal Operations Planning for SAR Satellite Constellations In Low Earth Orbit [C]// Proc. of the 6th International Symposium on Reducing the Costs of Spacecraft Ground Systems and Operations. ESA, 2005.
[66] Florio S D. Performances Optimization of Remote Sensing Satellite Constellations: a Heuristic Method [C]// Proc. of the International Workshop on Planning and Scheduling for Space. [s.n.], 2006.
[67] Gooley T D. Automating the Satellite Range Scheduling Process [D]. OH: Air Force Institute of Technology, 1993.
[68] Schalck S M. Automating Satellite Range Scheduling [D]. OH: Air Force Institute of Technology, 1993.
[69] Colin E B. Scheduling Deep-space Network Data Transmissions: a Lagrangian Relaxation Approach [C]// Proc. SPIE, 1993, Vol. 1963: 330-340.
[70] Parish D A. A Genetic Algorithm Approach to Automating Satellite Range Scheduling [D]. Air Force Institute of Technology, USA, 1994.
[71] Jang K. The Capacity of The Air Force Satellite Control Network [D]. OH: Air Force Institute of Technology, USA, 1996.
[72] Burrowbridge S E. Optimal Allocation of Satellite Networks Resource [D]. VA: Virginia Tech. University, 1999.
[73] Howe A E, Whitley L D, Watson J P, Barbulescu L. A Study of Air Force Satellite Access Scheduling [C]. Proc. of the World Automation Congress. [s.n.], 2000.
[74] Howe A E, Whitley L D, Barbulescu L, Watson J P. Mixed Initiative Scheduling for the Air Force Satellite Control Network [C]. Proc. of the 2nd International NASA Workshop on Planning and Scheduling for Space. [s.n.], 2000.
[75] Barbulescu L, Howe A E, Watson J P, et al. Satellite Range Scheduling: A Comparison of Genetic, Heuristic and Local Search [C]// LNCS 2439: Proc. of the 7th International Conference on Parallel Problem Solving from Nature. Springer, 2002.
[76] Barbulescu L,Watson J P,Whitley L D, Howe A E. Scheduling Space–Ground Communications for the Air Force Satellite Control Network [J]. Journal of Scheduling, 2004, 7(1): 1094-6136.
[77] Barbulescu L, Howe A E, Whitley L D, Roberts M. Trading places: How to Schedule More in a Multi-resource Oversubscribed Scheduling Problem [C]// Proc. of the International Conference on Planning and Scheduling. AAAI, 2004.
[78] Barbulescu L, Howe A E, Whitley L D, et al. AFSCN Scheduling: How the Problem and Solution Have Evolved [J]. Mathematical and Computer Modeling, 2006, 43(9): 1023-1037.
[79] Barbulescu L, Whitley L D, Howe A E. Leap Before You Look: An Effective Strategy in an Oversubscribed Scheduling Problem [C]// Proc. of the 9th National Conference on Artifficial Intelligence. AAAI, 2004.
[80] Roberts M, Whitley L D,Howe A E, Barbulescu L. Random Walks and Neighborhood Bias in Oversubscribed Scheduling [C]// Proc. of the 2nd Multidisciplinary International Conference on Scheduling: Theory and Applications. [s.n.], 2005.
[81] Rao J D, Soma P, Padmashree G S. Multi-Satellite Scheduling System for Leo Satellite Operations [C]// Proc. of the 5th International Conference on Space Operations. [s.n.], 1998.
[82] Soma P, Venkateswarlu S, Santhalakshmi S, et al. Multi-Satellite scheduling using Genetic Algorithms [C]. Proc. of the 8th International Conference on Space Operations. [s.n.], 2004.
[83]王远振,赵坚,聂成.多卫星-地面站系统的Petri网模型研究[J].空军工程大学学报(自然科学版), 2003, 4(2):7-10.
[84]王远振,赵坚,聂成.多星地面站设备优化调度方法研究[J].计算机仿真, 2003, 20(7): 17-19.
[85]王远振,高卫斌,聂成.多星地面站系统资源配置优化研究综述[J].系统工程与电子技术, 2004, 26(4):437-439.
[86]李云峰.卫星-地面站接收调度模型及算法研究[D].长沙:国防科技大学, 2007.
[87] Mancel C, Lopez P. Complex optimization problems in space systems [C]// Proc. of American Association for Aritficial Intelligence. AAAI, 2003.
[88] Marinelli F, Nocella S, Rossi F, et al. A Lagrangian Heuristic for Satellite Range Scheduling with Resource Constraints [R]. Technical Report TRCS 004, Universit`a di L’Aquila, 2005.
[89] Oddi A, Cesta A, Policella N, et al. Scheduling Downlink Operations in MARS-EXPRESS [C]// Proc. of the 3rd NASA Workshop on Planning and Scheduling. [s.n.], 2002.
[90] Oddi A, Policella N, Cesta A, et al. Generating High Quality Schedules for a Spacecraft Memory Downlink Problem [C]// LNCS 2833: CP 2003, 9th International Conference. Berlin: Springer, 2003: 570-584.
[91]方炎申.中继卫星调度模型研究[D].长沙:国防科技大学, 2007.
[92]任萱.军事航天技术[M].北京:国防工业出版社, 1999.
[93] Nieke J, Schwartzer H, Neumann A, et al. Imaging Spaceborne and Airborne Sensor Systems in the Beginning of the Next Century [C]// Conference on Sensors, Systems and Next Generation Satellites -the European Symposium on Aerospace Remote Sensing. III. SPIE, 1997, Vol: 3221.
[94]孙佳.国外合成孔径雷达卫星发展趋势分析[J].装备指挥技术学院学报, 2007, 18(1): 67-70.
[95]魏钟铨.合成孔径雷达卫星[M].北京:科学出版社, 2001.
[96]詹辛农. 21世纪航天数据存储技术展望[J].中国航天, 1997, No.2: 34-37.
[97]朱岩.基于闪存的星载高速大容量存储技术的研究[D].北京:中国科学院空间科学与应用研究中心, 2006
[98]王永东,崔容强,徐秀琴.空间太阳电池发展现状及展望[J].电源技术, 2001, 25(5): 182-185.
[99]鄢婉娟.太阳同步轨道卫星电源系统设计计算方法研究[J].中国空间科学技术, 2001, No.2: 19-25.
[100]中国首颗中继卫星“天链一号”01星发射成功[N].中国空间科学技术, 2008, No.6: 14.
[101]曾德贤,董绪荣,李睿. LEO卫星接入TDRSS的可行性分析与仿真[J].电子与信息学报, 2008, 30(4): 772-775.
[102]吕海寰,蔡剑铭,甘仲民.卫星通信系统[M].北京:中国邮电出版社, 1994.
[103]刘嘉兴.近空问跟踪与数据中继系统的初步设想[J].电讯技术, 2008, 48(5): 46-50.
[104] Verfaillie G, Lemaitre M, Bataille N, Lachiver J M. Management of the Mission of Earth Observation Satellites, Challenge Description [R]. Technical report, 2002.
[105] Garey M R, Johnson D S. Computers and Intractability: A Guide to the Theory of NP-Completeness [M]. W. H. Freeman and Company, 1979.
[106] Holland J H. Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press, 1975.
[107] Kramer L, Barbulescu L, Smith S. Analyzing Basic Representa-tion Choices in Oversubscribed Scheduling Problems [C]// Proc. of the 3rd Multidisciplinary International Conference on Scheduling: Theory and Application. [s.n.], 2007.
[108] Back T, Fogel D B, Michalewicz Z. Handbook of Evolutionary Computation [M], IOP Publishing Ltd and Oxford University Press, 1997.
[109] Goldberg D E. Genetic Algorithms in Search, Optimizaiton and Machine Learning [M]. MA: Addison-Wesley, 1989.
[110] Knuth D E. The Art of Computer Programming [M], Vol. 3, Second Edition. Addison Wesley, 1998.
[111] Coello C. Theoretical and Numerical Constraint-Handling Techniques Used with Evolutionary Algorithms: a Survey of the State of the Art [J]. Computer Methods in Applied Mechanics and Engineering, 2002, 191 (11/12): 1245-1287.
[112] Michalewicz Z, Dasgupta D, Le R, et al. Evolutionary Algorithms for Constrained Engineering Problems [J]. Computers & Industrial Engineering, 1996, 30(4): 851-970.
[113] Michalewicz Z, Schoenauer M. Evolutinary Computation for Constrained Parameter Optimization Problems [J]. Evolutionary Computation, 1996, 4(1): 1-32.
[114]余文,李文厚.遗传算法对约束优化问题的研究综述[J].计算机科学, 2002, 29(6): 98-101.
[115]王勇,蔡自兴,周育人,肖赤心.约束优化进化算法研究及其进展[J].软件学报, 2007, 18(11): 2691?2706.
[116] Campos V, Laguna M, Marti R. Context-Independent Scatter and Tabu Search for Permutation Problems [J]. INFORMS Journal on Computing, 2005, 17(1): 111-122.
[117] Bierwirth C, Mattfeld D C, Kopter Herbert. On Permutation Representations for Scheduling Problems [C]// LNCS 1141: Proc. of the 4th International Conference on Parallel Problem Solving from Nature. Berlin: Springer, 1996: 310-318.
[118]刑文训,谢金星.现代优化计算方法[M].北京:清华大学出版社, 2006.
[119] Whitley D, Watson J P. Complexity Theory and the No Free Lunch Theorem [M]. Search Methodologies: Introducting Tutorials in Optimization and Decision Support Thchnologies, Edited by E K Burke and G Kendall, Springer, 2006.
[120]王凌,郑大钟. Meta-heuristic算法研究进展[J].控制与决策, 2000, 15(3): 257-262.
[121] Christian Blum, Andrea Roli. Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison [J]. ACM Computing Surveys, 2003, 35(3): 268-308.
[122] Michel Gendreau, Jean-Yves Potvin. Metaheuristics in Combinatorial Optimization [J].Annals of Operations Research, 2005, 140: 189-213.
[123] Mitra D, Romeo F, Vincentelli A S. Convergence and Finite Time Behavior of Simulated Annealing [J]. Adv. Appl. Prob., 1986, 18: 747-771.
[124] Hajek B. Cooling Schedules for Optimal Annealing [J]. Math OperRes, 1988, 13(2): 311-329.
[125] Eiben A E, Aarts E H, Van Hee K M. Global Convergence of Genetic Algorithms: An Infinite Markov Chain Analysis [J]. Parallel Problem Solving from Nature. Berlin: Springer-Verlag, 1991: 4-12.
[126] Rudolph G. Convergence Properties of Canonical Genetic Algorithms [J]. IEEE Trans. on Neural Network, 1994, 5(1): 96-10.
[127]李超,谢端强,冯良贵,等.代数学基础[M].长沙:国防科技大学出版社, 2000.
[128] Mitchell M, Holland J H, Forrest S. When Will a Genetic Algorithm Outperform Hill Climbing [J]. Advances in Neural Information Processing Systems 6, 1993: 51-58
[129] Juels A, Wattenberg M. Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms [R]. Technical Report, CSD-94-834, Department of Computer Science, University of California at Berkeley, 1994.
[130] Kirkpatrick S, Gelatt C D, Vecchi M P. Optimization by Simulated Annealing [J]. Science, 1983, 220(4598): 671-680.
[131]玄光男,程润伟.遗传算法与工程设计[M].北京:科学出版社, 2000.
[132] Goldberg D, Lingle R. Alleles, loci, and the Traveling Salesman Problem [C]// Grefenstette J J. Proc. of International Conference on Genetic Algorithms and their Applications. Hillsdale, NJ: Lawrence Erlbaum Associates, 1985: 154-159.
[133] Davis L. Applying Adaptive Algorithms to Epistatic Domains [C]// IJCAI. Proc. of International Joint Conference on Artificial Intelligence. Morgan Kaufmann, 1985.
[134] Syswerda G. Schedule Optimization Using Genetic Algorithms [M]// Davis L. Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold, 1991: 332-349.
[135] Oliver I, Smith D, Holland J. A Study of Permutation Crossover Operators on the Traveling Salesman Problems [C]// Grefenstette J J. Proc. of 2nd International Conference on Genetic Algorithms and their Applications. Hillsdale, NJ: Lawrence Erlbaum Associates, 1987: 224-230.
[136] Syswerda G. Uniform crossover in genetic algorithms [C]// Schaffer J D. Proc. of the 3rd International Conference on Genetic Algorithms. San Mateo, CA, USA: Morgan Kaufmann, 1989: 2-9.
[137] Whitley D. The GENITOR Algorithm and Selective Pressure: Why Rank-Based Allocation of Reproductive Trials is Best [C]// Schaffer J D. Proc. of the 3rd InternationalConference on Genetic Algorithms. San Mateo, CA, USA: Morgan Kaufmann, 1989: 116-121.
[138] Glover F. Surrogate Constraint Duality in Mathematical Programming [J]. Operations Research, 1975, 23(3): 434-451.
[139] Geoffrion A M, Lagrangian Relaxation for Integer Programming [J]. Mathematical Programming Study, 1974: 82-114.
[140] Fisher M L. The Lagrangian Relaxation Method for Solving Integer Programming Problems [J]. Management Science, 1981, 27(1): 1-18
[141] Arnaud F. The Multidimensional 0-1 Knapsack Problem: An Overview [J]. European Journal of Operational Research, 2004, 155: 1-12.
[142] Ruedee Rangsaritratsamee, William G. Ferrell Jr., Mary Beth Kurz. Dynamic Rescheduling That Simultaneously Considers Efficiency and Stability [J]. Computers & Industrial Engineering, 2004, 46: 1-15.