考虑任务合成的成像卫星调度模型与优化算法研究
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摘要
成像卫星是一类从太空中获取地面图像信息的对地观测卫星,在军事和经济等领域发挥了重要作用。随着成像卫星数量增多,成像任务需求也呈现出多样化、复杂化和快速增长趋势,如何根据各类成像任务需要和现有卫星资源能力,联合制定多颗卫星的优化成像方案,是提高卫星观测效率,充分发挥卫星系统整体效能的关键问题。
许多成像卫星的侧摆机动性能较差,每个轨道圈次内的侧视成像次数有限,为提高该类卫星的观测效率,必须考虑将满足一定条件的任务合成并安排卫星观测。另外,成像任务分为点和区域目标两类目标,应用中需要将两类目标综合调度。本文研究了考虑任务合成的成像卫星调度问题,其是在满足资源和任务约束条件下,综合调度点和区域两类目标,并充分考虑任务间的优化合成,以制定成像卫星的调度方案并实现最大完成任务的目的。论文基于建模方法和优化理论,从问题模型和优化算法两方面开展研究,主要工作及创新点如下:
(1)建立了考虑任务合成的成像卫星调度模型
在深入分析卫星对各类目标的调度特点以及任务合成特性的基础上,建立了考虑任务合成的成像卫星调度模型。首先提出了区域目标动态分解方法,将区域分解为多个子任务,然后,将点目标视为特殊的“区域”,按照观测机会进行分解,并将两类目标统一为元任务。定义合成任务描述任务合成关系,并分析了合成任务与元任务的关系。最后,为两类目标分别构建收益函数,建立了考虑任务合成的成像卫星调度模型。论文建立的模型在综合调度两类目标的基础上,考虑了任务合成的优化因素,与其它的简化模型相比,更具有实际应用价值。
(2)提出了基于整体优化策略的求解算法
对问题进行整体建模后,提出了两种对问题进行整体优化求解的算法:动态合成启发式算法与快速模拟退火算法。首先定义了解的编码、基本邻域和评价函数等基础组件,为设计并实现多种算法提供支持。动态合成启发式算法中,提出了任务需求度、资源竞争度、时间窗口竞争度等参数指标,为任务选择最小冲突的卫星资源及时间窗口;提出了任务合成启发式选择任务的合成位置,使卫星能够以最小的侧视角度或最小的数据冗余对多个任务合成观测。快速模拟退火算法中,设计了多种邻域结构,实现对元任务的合成和合成任务的分解操作。采用“冒险”的接受概率和快速退火计划,提高算法的求解速度,同时,采用回火机制及多种分化策略,避免算法陷入局部最优。实验证明,动态合成启发式算法的速度较快,而快速模拟退火算法的结果更优。
(3)提出了基于分解优化策略的求解算法
为提高问题规模较大时的求解效率,借鉴大系统的“分解-协调”思想,提出了基于分解优化策略的求解算法。将问题分解为任务分配与任务合成两个子问题,任务分配为任务选择卫星资源及时间窗口,任务合成则针对该分配方案进行最优合成。建立任务合成的最大覆盖模型,并提出了基于动态规划的最优合成算法,能够在多项式时间内求得最优合成方案。采用蚁群算法求解任务分配问题,通过自适应参数调整及信息素平滑策略,实现全局搜索和快速收敛间的平衡。蚁群算法接收任务合成结果反馈,并引导蚁群搜索优化的任务分配方案。通过子问题之间的协调优化,得到了优化的任务合成观测方案。实验证明,该算法对大规模问题求解的性能较高。
Imaging satellites are a kind of Earth Observation Satellites (EOS) orbit the earth. The mission of imaging satellite is to acquire images of specified areas on the earth surface to satisfy customers’observation requests. It plays important roles in military reconnaissance and economy. The observation scheduling becomes very complicated with the evolution of both satellites and observation requests. How to coordinate the satellites observation is becoming increasingly important, which is the key issue to improve the efficiency of satellites observation and make full use of satellites.
Some of satellites have rigid constraints in slew activities, which limit the satellites’observing activities. It takes much time for the satellite to maneuver from its previous angle to the desired angle. The maneuvering time depend on the positions and postures of the two consecutive imaging operations. If there is no enough time, the satellite can not maneuver successfully. According to the area of targets, the observation tasks can be labeled by spot targets or polygon targets. And the two types of the targets must be scheduled together to enhance the satellites’efficiency. In this dissertation, Imaging Satellites Scheduling Problem with Task Merging (ISSP-TM) is studied. This problem mainly solves two puzzles: how to schedule two kinds of targets together and how to get a good task merging solution. Properly modeling and solving are the key issues to solve this problem. Based on the modeling theory and optimization theory, this dissertation studies the model and algorithms for this problem. The main work and contributions are as follows:
(1) A mathematic model for ISSP-TM is presented. The dissertation first analyzed the characters of satellites observation scheduling towards spot and polygon targets. To improve the accuracy and efficiency of the segmenting of polygon targets, the dissertation proposed a dynamic segmenting method. The spot target is regarded as a special polygon and can be divided into subtasks, which are generated according to the time widows of the target. One subtask is related to one time window. In order to evaluate the profit of targets the dissertation introduced two evaluation functions. Composite task is defined to character the task merging observation. Compared with the models formulated in previous studies, the model we present would be more valuable for satellites observation applications.
(2) Two optimization algorithms were proposed to solve the problem based on integrating optimization strategy. The foundational components, such as basic neighborhoods and evaluation functions, were defined to support the design and realization of optimization algorithms.
In Dynamic Task Merging Heuristic algorithm (DTMH), the contention heuristics including task need heuristic, resource contention heuristic and time window contention heuristic are adopted to select the minimized contention satellite resources and time slots. The task merging heuristic is proposed to select the most suitable task merging position, which ensures that satellites can observe tasks with smaller slew angles and minimize the waste of resources brought by the merging observations.
Very Fast Simulated Annealing algorithm (VFSA) is also developed to solve this problem. Multiple neighborhoods are defined for dynamic task merging and decomposing in search procedure. With an adventure acceptable probability and fast annealing, VFSA can improve the convergence speed. Re-annealing mechanism and diversification strategies were defined to avoid the local optimum solutions and exploit the larger space. Computation results demonstrated that the DTMH was very fast, but not so efficient. The VFSA could outperform the DMTH, but expense of extensive run times.
(3) Decomposition optimization algorithm is proposed to optimize the complex problems. The problem is divided into two sub-problems: task assignment problem and task merging problem. Task assignment problem allocates satellite resource and time window for task and task merging problem generate the best merging solution. The Maximal Covering Location Problem model is proposed, and a polynomial optimization algorithm based on dynamic programming is developed to find the best merging solution. In task assignment phase, we propose an adaptive ant colony optimization algorithm to select the specific satellite and the specific time window for each task. Adaptive parameter adjusting and pheromone trail smoothing strategies are introduced to balance the exploration and the exploitation of search. The result of task merging is feedback to the ant colony, which can guide the search process of the ant colony optimization algorithm. Computation results demonstrate the effectiveness of our algorithm.
许多成像卫星的侧摆机动性能较差,每个轨道圈次内的侧视成像次数有限,为提高该类卫星的观测效率,必须考虑将满足一定条件的任务合成并安排卫星观测。另外,成像任务分为点和区域目标两类目标,应用中需要将两类目标综合调度。本文研究了考虑任务合成的成像卫星调度问题,其是在满足资源和任务约束条件下,综合调度点和区域两类目标,并充分考虑任务间的优化合成,以制定成像卫星的调度方案并实现最大完成任务的目的。论文基于建模方法和优化理论,从问题模型和优化算法两方面开展研究,主要工作及创新点如下:
(1)建立了考虑任务合成的成像卫星调度模型
在深入分析卫星对各类目标的调度特点以及任务合成特性的基础上,建立了考虑任务合成的成像卫星调度模型。首先提出了区域目标动态分解方法,将区域分解为多个子任务,然后,将点目标视为特殊的“区域”,按照观测机会进行分解,并将两类目标统一为元任务。定义合成任务描述任务合成关系,并分析了合成任务与元任务的关系。最后,为两类目标分别构建收益函数,建立了考虑任务合成的成像卫星调度模型。论文建立的模型在综合调度两类目标的基础上,考虑了任务合成的优化因素,与其它的简化模型相比,更具有实际应用价值。
(2)提出了基于整体优化策略的求解算法
对问题进行整体建模后,提出了两种对问题进行整体优化求解的算法:动态合成启发式算法与快速模拟退火算法。首先定义了解的编码、基本邻域和评价函数等基础组件,为设计并实现多种算法提供支持。动态合成启发式算法中,提出了任务需求度、资源竞争度、时间窗口竞争度等参数指标,为任务选择最小冲突的卫星资源及时间窗口;提出了任务合成启发式选择任务的合成位置,使卫星能够以最小的侧视角度或最小的数据冗余对多个任务合成观测。快速模拟退火算法中,设计了多种邻域结构,实现对元任务的合成和合成任务的分解操作。采用“冒险”的接受概率和快速退火计划,提高算法的求解速度,同时,采用回火机制及多种分化策略,避免算法陷入局部最优。实验证明,动态合成启发式算法的速度较快,而快速模拟退火算法的结果更优。
(3)提出了基于分解优化策略的求解算法
为提高问题规模较大时的求解效率,借鉴大系统的“分解-协调”思想,提出了基于分解优化策略的求解算法。将问题分解为任务分配与任务合成两个子问题,任务分配为任务选择卫星资源及时间窗口,任务合成则针对该分配方案进行最优合成。建立任务合成的最大覆盖模型,并提出了基于动态规划的最优合成算法,能够在多项式时间内求得最优合成方案。采用蚁群算法求解任务分配问题,通过自适应参数调整及信息素平滑策略,实现全局搜索和快速收敛间的平衡。蚁群算法接收任务合成结果反馈,并引导蚁群搜索优化的任务分配方案。通过子问题之间的协调优化,得到了优化的任务合成观测方案。实验证明,该算法对大规模问题求解的性能较高。
Imaging satellites are a kind of Earth Observation Satellites (EOS) orbit the earth. The mission of imaging satellite is to acquire images of specified areas on the earth surface to satisfy customers’observation requests. It plays important roles in military reconnaissance and economy. The observation scheduling becomes very complicated with the evolution of both satellites and observation requests. How to coordinate the satellites observation is becoming increasingly important, which is the key issue to improve the efficiency of satellites observation and make full use of satellites.
Some of satellites have rigid constraints in slew activities, which limit the satellites’observing activities. It takes much time for the satellite to maneuver from its previous angle to the desired angle. The maneuvering time depend on the positions and postures of the two consecutive imaging operations. If there is no enough time, the satellite can not maneuver successfully. According to the area of targets, the observation tasks can be labeled by spot targets or polygon targets. And the two types of the targets must be scheduled together to enhance the satellites’efficiency. In this dissertation, Imaging Satellites Scheduling Problem with Task Merging (ISSP-TM) is studied. This problem mainly solves two puzzles: how to schedule two kinds of targets together and how to get a good task merging solution. Properly modeling and solving are the key issues to solve this problem. Based on the modeling theory and optimization theory, this dissertation studies the model and algorithms for this problem. The main work and contributions are as follows:
(1) A mathematic model for ISSP-TM is presented. The dissertation first analyzed the characters of satellites observation scheduling towards spot and polygon targets. To improve the accuracy and efficiency of the segmenting of polygon targets, the dissertation proposed a dynamic segmenting method. The spot target is regarded as a special polygon and can be divided into subtasks, which are generated according to the time widows of the target. One subtask is related to one time window. In order to evaluate the profit of targets the dissertation introduced two evaluation functions. Composite task is defined to character the task merging observation. Compared with the models formulated in previous studies, the model we present would be more valuable for satellites observation applications.
(2) Two optimization algorithms were proposed to solve the problem based on integrating optimization strategy. The foundational components, such as basic neighborhoods and evaluation functions, were defined to support the design and realization of optimization algorithms.
In Dynamic Task Merging Heuristic algorithm (DTMH), the contention heuristics including task need heuristic, resource contention heuristic and time window contention heuristic are adopted to select the minimized contention satellite resources and time slots. The task merging heuristic is proposed to select the most suitable task merging position, which ensures that satellites can observe tasks with smaller slew angles and minimize the waste of resources brought by the merging observations.
Very Fast Simulated Annealing algorithm (VFSA) is also developed to solve this problem. Multiple neighborhoods are defined for dynamic task merging and decomposing in search procedure. With an adventure acceptable probability and fast annealing, VFSA can improve the convergence speed. Re-annealing mechanism and diversification strategies were defined to avoid the local optimum solutions and exploit the larger space. Computation results demonstrated that the DTMH was very fast, but not so efficient. The VFSA could outperform the DMTH, but expense of extensive run times.
(3) Decomposition optimization algorithm is proposed to optimize the complex problems. The problem is divided into two sub-problems: task assignment problem and task merging problem. Task assignment problem allocates satellite resource and time window for task and task merging problem generate the best merging solution. The Maximal Covering Location Problem model is proposed, and a polynomial optimization algorithm based on dynamic programming is developed to find the best merging solution. In task assignment phase, we propose an adaptive ant colony optimization algorithm to select the specific satellite and the specific time window for each task. Adaptive parameter adjusting and pheromone trail smoothing strategies are introduced to balance the exploration and the exploitation of search. The result of task merging is feedback to the ant colony, which can guide the search process of the ant colony optimization algorithm. Computation results demonstrate the effectiveness of our algorithm.
引文
[1]总装备部卫星有效载荷及应用技术专业组应用技术分组.卫星应用现状与发展.北京:中国科学技术出版社, 2001.
[2]王永刚,刘玉文.军事卫星及应用概论.北京:国防工业出版社, 2003.
[3]任萱.军事航天技术.北京:国防工业出版社, 1999.
[4]曾华锋,夏洪流,周刚.现代侦察监视技术.北京:国防工业出版社, 2001.
[5]张钧屏,方艾里,万志龙.对地观测与对空监视.北京:科学出版社, 2001.
[6]冯钟葵,石丹,陈文熙,骆艾荣.法国遥感卫星的发展——从SPOT到Pleiades.遥感信息, 2007, 4: 87-92.
[7]孙长喜,魏攀科.国外照相侦察卫星发展综述.国防科技, 2007, 14 (2): 39-41.
[8]梁巍,周润松.国外侦察卫星最新进展.航天器工程, 2007, 16 (2): 30-40.
[9] W. E. STONEY. GUIDE TO LAND IMAGING SATELLITES. http://www.asprs.org, 2008.
[10] E Bensana, G Verfaillie, J C Agnese, N Bataille. Exact and Approximate Methods for the Daily Management of an Earth Observing Satellite. Symposium on Space Mission Operations and Ground Data Systems. Munich, Germany,1996.
[11] Cecile Murat Virginie Gabrel. MP for Earth Observation Satellite Mission Planning. Operations Research in Space and Air,Series: Applied Optimization, 2003.
[12] Dezhen Song, A.Frank, van der Stappen, K. Goldberg. An Exact Algorithm Optimizing Coverage-Resolution for Automated Satellite Frame Selection. Proc of the IEEE International Conference on Robotics and Automation. 2004.63-70.
[13] Wolfe W.J, Sorensen S.E. Three Scheduling Algorithms Applied to the Earth Observing Systems Domain Management Science, 2000, 46 (1): 148-168.
[14] Vasquez M, Hao. J.-K. A "Logic-Constrained" Knapsack Formulation and a Tabu Algorithm for the Daily Photograph Scheduling of an Earth Observation Satellite. Journal of autational Optimization and Applications, 2000.
[15] J Pemberton, Flavius Galiber. A Constraint-Based Approach to Satellite Scheduling. Proceedings of Constraint Programming and Large Scale Discrete Optimization. Piscataway, New Jersey, United States,2001.
[16] J Pemberton. Towards scheduling over-constrained remote sensing satellites. Proceedings of the 2nd International Workshop on Planning and Scheduling for Space. San Francisco, CA,2000.
[17] Nicholas G.Hall, Michael J.Magazine. Maximizing the value of a space mission. European Journal of Operational Research, 1994, 78 (2): 224-241.
[18] Wei-Cheng Lin, Da-Yin Liao, Chung-Yang Liu, Yong-Yao Lee. Daily Imaging Scheduling of an Earth Observation Satellite. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, 2005, 35 (2): 213-223.
[19] Gabrel V, Vanderpooten D. Enumeration and interactive selection of efficient paths in a multiple criteria graph for scheduling an earth observing satellite. European Journal of Operational Research, 2002, 139 (3): 533~542.
[20] Verfaillie G, Bensana E, Edery C.M, Bataillie N. Dealing with Uncertainty When Managing Earth Observing Satellites. Proceedings of i-SAIRAS-99. Netherlands: Noordwijk,1999.
[21] Damiani S, Verfaillie G, Charmeau M C. A Continuous Anytime Planning Module for an Autonomous Earth Watching Satellite. Proc. of the ICAPS-05 Workshop on Planning under Uncertainty for Autonomous Systems. Monterey, CA,2005.
[22] Potter W. A Photo Album of Earth: Scheduling Landsat 7 Mission Daily Activities. Proceedings of the International Symposium Space Mission Operations and Ground Data Systems. Japan,Tokyo,1998.
[23] R Sherwood, A Govindjee, D Yan, G Rabideau. Using ASPEN to Automate EO-1 Activity Planning. Proceedings of the 1998 IEEE Aerospace Conference. Colorado,1998.
[24] R Sherwood, A Govindjee, D Yan. Aspen: EO-1 Mission Activity Planning Made Easy. NASA Workshop on Planning and Scheduling for Space. 1997.
[25] Alex S. Fukunaga, S Chien, D Yan. Aspen: A Framework for Automated Planning and Scheduling of Spacecraft Control and Operations. The Symposium on AI, Robotics and Automation in Space. Tokyo, Japan,1997.
[26] S Chien, G. Rabideau, R. Knig, G. Rabideau. Aspen-Automated Planning and Scheduling for Space Mission Operations. In 6th International Symposium on Space missions Operations and Ground Data Systems. 2000.
[27] R.H. Cohen. Automated Spacecraft Scheduling - the Aster Example. Jet propulsion Laboratory: Ground System Architectures Workshop, 2002.
[28] H. Muraoka, R.H. Cohen, T. Ohno, N. Doi. ASTER Observation Scheduling Algorithm. Proceedings of the 5th International Symposium on Space Mission Operations and Ground Data Systems. Tokyo,Japan,1998.
[29] Alexander F Herz, Adeena Mignogna. Collection Planning for the OrbView-3 High Resolution Imaging Satellite. SpaceOps 2006. Rome,Italy,2006.
[30] Al Globus, James Crawford, Jason Lohn, Robert Morris. Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach. Proceedings of the 3rd International NASA Workshop on Planning and Scheduling for Space. NASA,2002.
[31] Jeremy Frank, Ari Jonsson, Robert Morris, David Smith. Planning andScheduling for Fleets of Earth Observing Satellites. Proceeding of the 6th International Symposium on Artificial Intelligence, Robotics, Automation and Space 2002. Montreal,2002.
[32] Robert A. Morris, Jennifer L. Dungan, John L. Bresina. An Information Infrastructure for Coordinating Earth Science Observations. Proceeding of 2nd IEEE International Conference on Space Mission Challenges for Information Technology. Pasadena, CA,2006.
[33] Morris R, Dungan J, Edgington W, Williams J. Coordinated Science Campaign Scheduling for Sensor Webs. Proceedings of the 8th International Symposium on Artificial Intelligence, Robotics, and Automation in Space. Munchen, Germany,2005.
[34] Jackson Sarah E. Planning Coverage of Points of Interest Via Multiple Imaging Surveillance Assets: A Mulit-Modal Approach. Master's thesis, Air Force Institute of Technology (AFIT), 2003.
[35] Veridian Inc. Generic Resource Event and Activity Scheduler. 2003.
[36] Analytical Graphics Inc. Satellite Tool Kit 5.0. 2003.
[37] Space Imaging Inc. Collection Planning System: A Multi-Source Satellite Imagery Collection Optimization Application. 2003.
[38] P. Mougnaud, L. Galli, C. Castellani, I. Famoso. MAT a Multi-mission Analysis and Planning Tool for Earth Observation Satellite Constellations. The 9th International Conference on Space Operations( SpaceOps 2006). Rome, Italy,2006.
[39] Nicola Bianchessi. Planning and Scheduling Problems for Earth Observation Satellites: Models and Algorithms. Italy: Dipartimento di Tecnologie dell, 2005.
[40] Nicola Bianchessi, Jean-Francois Cordeau, Jacques Desrosiers, Gilbert Laporte. A Heuristic for the Multi-Satellite, Multi-Orbit and Multi-User Management of Earth Observation Satellites. European Journal of Operational Research, 2007, 177 (2): 750-762.
[41] Nicola Bianchessi, Giovanni Righini. Planning and scheduling algorithms for the COSMO-SkyMed constellation. Aerospace Science and Technology, 2008, 12 (7): 535-544.
[42]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.
[43]陈站华.资源卫星任务安排测试方法的研究与实现.无线电工程, 2005, 35 (3): 62-64.
[44]代树武.航天器自主运行关键技术的研究北京:中国科学院空间科学与应用研究中心博士后论文, 2002.
[45]代树武,孙辉先.卫星的智能规划与调度.控制与决策, 2003, 18 (2): 203-206.
[46]刘洋,代树武.卫星有效载荷的规划与调度.华北航天工业学院学报, 2004, 14 (2): 1-4.
[47]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学位论文, 2004.
[48]李菊芳.航天侦察多星多地面站任务规划问题研究.长沙:国防科技大学博士学位论文, 2005.
[49]王均.成像卫星综合任务调度模型与优化方法研究.长沙:国防科技大学博士学位论文, 2007.
[50]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:国防科技大学博士学位论文, 2005.
[51]刘洋.成像侦察卫星动态重调度模型、算法及应用研究.长沙:国防科技大学博士学位论文, 2005.
[52]王军民.成像卫星鲁棒性调度方法及应用研究.长沙:国防科技大学博士学位论文, 2008.
[53] T Beker, G Chechik, I Meilijson, E Ruppin. PlanSat - A Novel Solution for Imaging Satellite Scheduling. White paper. Soligence Corp.
[54] J Dungan, J Frank, A Jonsson, R Morris. Advances in Planning and Scheduling of Remote Sensing Instruments for Fleets of Earth Orbiting Satellites. Proceedings of the 2nd Annual Earth Science Technology Conference. Pasadena, California,2002.
[55] Harrison S A, Price M E. Task Scheduling for Satellite Based Imagery. Proceedings of the Eighteenth Workshop of the UK Planning and Scheduling Special Interest Group. University of Salford, UK, 1999.64-78.
[56] Verfaillie G. Russian Doll Search for Solving Constraint Optimization Problems. Proceedings of AAAI-96. Portland: Oregon,1996.181-187.
[57]徐雪仁,宫鹏,黄学智,金勇.资源卫星(可见光)遥感数据获取任务调度优化算法研究.遥感学报, 2007, 11 (1): 109-114.
[58] John L. Bresina. Heuristic-Biased Stochastic Sampling. Proceedings of the Thirteenth National Conference on Artificial Intelligence. Portland, Oregon,1996.
[59] Angelo Oddi, Amedeo Cesta, Nicola Policella, Gabriella Cortellessa. Scheduling Downlink Operations in MARS-EXPRESS. Proceedings of the 3rd NASA Workshop on Planning and Scheduling. Huston,2002.
[60] Cordeau J-F, Laporte G. Maximizing the Value of an Earth Observation Satellite Orbit. Journal of the Operational Research Society, 2005, 56 (8): 962-968.
[61]李菊芳,谭跃进.卫星观测系统整体调度问题的建模和求解.系统工程理论与实践, 2004, 24 (12): 65-71.
[62] Kuipers EJ. Algorithm for the management of the missions of Earth observation satellites. Fifth ROADEF Annual Conference. Avignon , France,2003.
[63] Parish D A. A Genetic Algorithms Approach to Automating Satellite Range Scheduling. Master’s thesis, air force institute of technology, 1994.
[64] Piero P. Bonissone, Raj Subbu, Neil Eklund, Thomas R. Kiehl. Evolutionary Algorithms + Domain Knowledge = Real-World Evolutionary Computation. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2006, 10 (3): 256-280.
[65] Al. Globus, J. Crawford, J. Lohn, A. Pryor. A Comparison of Techniques for Scheduling Earth Observing Satellites. AAAI. 2004.836-843.
[66] J. Walton. Models for the Management of Satellite-based Sensors. Ph.D. Dissertation, Massachusetts Institute of Technology, 1993.
[67] M. Lema?tre, G. Verfaillie. Daily Management of an Earth Observation Satellite: Comparison of ILOG Solver with Dedicated Algorithms for Valued Constraint Satisfaction Problems. Third ILOG International Users Meeting. Paris,1997.
[68] Michel Lema?tre, Gérard Verfaillie, Frank Jouhaud, Jean-Michel Lachiver. Selecting and Scheduling Observations of Agile Satellites. Aerospace Science and Technology, 2002, 6 (5): 367-381.
[69] Mancel C. Complex Optimization Problems in Space Systems. 13Th International Conference on Automated Planning&Scheduling. Trento,Italy,2003.
[70] Claire Rivett, Carmine Pontecorvo. Improving Satellite Surveillance Through Optimal Assignment of Assets. Australian Government Department of Defence: DSTO-TR-1488, 2004.
[71]李曦.多星区域观测任务的效率优化方法研究.长沙:国防科技大学硕士学位论文, 2005.
[72]祝江汉,李曦,毛赤龙,张利宁.多卫星区域观测任务的侧摆方案优化方法研究.武汉大学学报(信息科学版), 2006, 31 (10): 868-870.
[73]阮启明,谭跃进,李永太,陈英武.基于约束满足的多星对区域目标观测活动协同.宇航学报, 2007, 28 (1): 238-242.
[74]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.
[75]阮启明,谭跃进,李菊芳,陈英武.对地观测卫星的区域目标分割与优选问题研究.测绘科学, 2006, 34 (1): 98-100.
[76] The Landsat Program - WRS. http://landsat.gsfc.nasa.gov/about/wrs.html.
[77] The SPOT Grid Reference System (GRS). http://www.crisp.nus.edu.sg/spot/spot.html.
[78]寇连群.印度ResourceSat-1(P-1)卫星介绍.印度ResourceSat-1(P-1)卫星介绍.中国科学院中国遥感卫星地面站, 2005.
[79] http://www.nspo.org.tw/c60/rocsat2/rs2_property.html.
[80]朱述龙,张占睦.遥感图象获取与分析.北京:科学出版社, 2003.
[81] Short N M. Historical and Technical Perspectives of Remote Sensing,http://rst.gsfc.nasa.gov/. 2004.
[82]文沃根.高分辨率IKONOS卫星影像及其产品的特性.遥感信息, 2001, 9 (1): 37-38.
[83] Gordon Petrie. Monitoring Iraq– Imagery Options for Monitoring and Gathering Intelligence. GI News, 2003: 1-2.
[84]周军.航天器控制原理.西安:西北工业大学出版社, 2001.
[85] Philippe Baptiste, W P M Nuijten. Constraint based scheduling: Applying Constraint Programming to scheduling problems. Kluwer Academic Press, 2001.
[86] E.H.L. Aarts, P.J.M. van Laarhoven, J.K. Lenstra, N.J.L. Ulder. A Computational Study of Local Search Algorithms for Job Shop Scheduling. Journal on Computing, 1994, 6 (2): 118-125.
[87] D Applegate, W Cook. A Computational Study of the Job Shop Scheduling Problem. Journal on Computing, 1991, 3 (3): 149-156.
[88] Torsten Fahle, Ulrich Junker, Stefan E Karisch, Niklas Kohi. Constraint Programming Based Column Generation for Crew Assignment. Journal of Heuristics, 2002, 8 (1): 59-81.
[89] Andersson E, E Houson, N Kohl, D Wedelin. Crew Pairing Optimization, Operational Research in the Airline Industry. International Series in Operational Research and Management Science, 1998, 9: 228-258.
[90] Laura Barbulescu, Adele E. Howe, L. Darrell Whitley, Mark Roberts. Understanding Algorithm Performance on an Oversubscribed Scheduling Application. Journal of Artificial Intelligence Research, 2006, 27: 577-615.
[91] Laura Barbulescu, Adele E. Howe, L. Darrell Whitley, Mark Roberts. Trading Places: How to Schedule More in a Multi-Resource Oversubscribed Scheduling Problem. 2003.
[92]何红艳,乌崇德,王小勇.侧摆对卫星及CCD相机系统参数的影响和分析.航天返回与遥感, 2003, 24 (4): 14-18.
[93]郗晓宁,王威,高玉东.近地航天器轨道基础.长沙:国防科技大学出版社, 2003.
[94]国立中央大学太空及遥测中心. http://www.csrsr.ncu.edu.tw.
[95]胡毓钜,龚剑文,黄伟.地图投影.北京:测绘出版社, 1981.
[96]钱曾波,刘静宇,肖国超.航天摄影测量.北京:解放军出版社, 1992.
[97]朱华统.常用大地坐标系及其变换.北京:解放军出版社, 1990.
[98]朱华统,杨元喜,吕志平. GPS坐标系统的变换.北京:测绘出版社, 1994.
[99] MapInfo Corporation. MapXtreme2005中文开发指南. 2006.
[100] G′erard Verfaillie, Michel Lema??tre. Planning activities for Earth watching and observing satellites and constellations. Proceedings of the Sixteenth International Conference on Automated Planning and Scheduling(ICAPS 2006). Cumbria, UK,2006.
[101] Rojanasoonthon Siwate. Parallel Machine Scheduling with Time Windows. University of Texas,Ph.D Thesis, 2003.
[102]李曙光.批调度与网络问题的组合算法.济南:山东大学博士学位论文, 2007.
[103] C.H.Potts, M.Y.Kovalyov. Scheduling with batching: a review. European Journal of Operational Research, 2000, 120 (2): 228-249.
[104] AZIZOGLU M, WEBSTER S. Scheduling A batch processing machine with incompatible job families. Computers & Industrial Engineering, 2001, 39 (3): 325-335.
[105]王晓峰,谷寒雨.一种改进的半导体生产线批处理机调度策略研究.计算机集成制造系统, 2007, 13 (6): 1115-1120.
[106] DOBSON G, NAMBIMADOM RS. The batch loading and scheduling problem. Operations Rosearch, 2001, 49 (1): 52-65.
[107] MATHIRAJAN M, IYER S A, CHANDRU V. Scheduling algorithms and sensitivity analysis for heterogeneous batch processors with incompatible job families. Journal of the Chinese lnstitute of lndustrial Engineers, 2004, 21 (1): 18-26.
[108] MATHIRAJAN M, SlVAKUMAR A L. Minimizing total weighted tardiness on heterogeneous batch Processing machine with incompatible job families. The lnternational Jourual of Advanced Manufacturing Technology, 2006, 21 (9): 1038-1047.
[109]金锋,宋士吉,杨建华,吴澄.染整车间染缸优化调度算法研究.计算机集成制造系统, 2008, 14 (3): 543-547.
[110] DEVPURA A, FOWLER J W, CARLYLE MW. Minimizing total weighted tardiness on single batch process machine with incompatible job families. Proceedings of the Symposiym on Operations Research 2000. Berlin,Germany,Springer-Verlag, 2000.366-371.
[111]王振国,陈小前,罗文彩,张卫华等.飞行器多学科设计优化理论与应用研究.北京:国防工业出版社, 2006.
[112]尚文利.车间计划调度集成模型研究.沈阳:中国科学院沈阳自动化研究所博士学位论文, 2005.
[113]王凌,郑大钟.邻域搜索算法的统一结构和混合优化策略.清华大学学报(自然科学版), 2000, 40 (9): 125-128.
[114]玄光南,程润伟.遗传算法与工程优化.北京:清华大学出版社, 2004.
[115] Lau, Hoong Chuin, Melvyn Sim, Kwong Meng Teo. Vehicle Routing Problem With Time Windows and a Limited Number of Vehicles. European Journal of Operational Research, 2003, 148 559-569.
[116] ILOG. ILOG Dispatcher 4.0 User's Manual. 2003.
[117] Metropolis N, Rosenbluth A, Rosenbluth M. Equation of state calculations by fast computing machines. Journal of Chemical Physics, 1953, 21: 1087-1092.
[118] Kirkpatrick S, Gelatt Jr C D, Vecchi M P. Optimization by simulated annealing. Science, 1983, 220: 671-680.
[119]邢文训,谢金星.现代优化计算方法.北京:清华大学出版社, 2005.
[120] B Suman, P Kumar. A survey of simulated annealing as a tool for single and multiobjective optimization. Journal of the Operational Research Society, 2006, 57 (10): 1143-1160.
[121]陈华根,吴健生,王家林,陈冰.模拟退火算法机理研究.同济大学学报(自然科学版), 2004, 32 (6): 802-805.
[122] Ingber L. Very Fast Simulated Re-annealing. Math Compute Modelling, 1989, 12: 967-973.
[123] Sudarshan Banerjee, Nikil Dutt. R. Very fast Simulated Annealing for HW-SW partitioning. Technical Report ,CECS-TR-04-17. UC,Irvine,2004.
[124]陈华根,李丽华,许惠平,陈冰.改进的非常快速模拟退火算法.同济大学学报(自然科学版), 2006, 8 (34): 1121-1125.
[125] Siwate Rojanasoonthon, Jonathan Bard. A GRASP for Parallel Machine Scheduling with Time Windows. INFORMS Journal on Computing, 2005, 17 (1): 32-52.
[126]李建,张永.一类集散货物路线问题的禁忌搜索算法设计.系统工程理论与实践, 2007, 27 (6): 117-123.
[127] Ali Manzak, Huseyin Goksu. Application of Very Fast Simulated Reannealing (VFSR) to Low Power Design. Embedded Computer Systems: Architectures, Modeling, and Simulation. Berlin Heidelberg,Springer-Verlag 2005.308-313.
[128] A. Anagnostopoulos, L. Michel, P. Van Hentenryck, Y. Vergados. A Simulated Annealing Approach to the Traveling Tournament Problem. Journal of Scheduling, 2006, 6 (2): 177-193.
[129] M.M.Solomon. Algorithms for Vehicle Routing and Scheduling Problems with Time Window Constraints. Operations Reasearch, 1987, 35 (2): 254-265.
[130] Galvao R D, Espejo L G A, Boffey B. A comparison of Lagrangean and surrogaterelaxations for the maximal covering location problem. European Journal of Operational Research, 2000, 24 (1): 377-389.
[131] Schilling D A, Vaidyanathan J, Barkhi L R. A review of covering problems in facility location. Location Science, 1993, 1: 25-55.
[132] Megiddo N, Zemel E, Hakimi S L. The maximum coverage location problem. SIAM Journal on Algebraic and Discrete Methods, 1983, 4 (2): 253-261.
[133] Daskin M S. Network and discrete location: models algorithms and applications. Network and discrete location: models algorithms and applications. NewYork: Wiley Interscience, 1995.
[134] Colorni A, Dorigo M, Maniezzo V. Distributed Optimization by Ant Colonies. Proceedings of European Conference on Artificial Life. France,1991.
[135] Dorigo M. Optimization, learning and natural algorithms. PhD thesis,Dipartimento di Elettronica, Politecnico di Milano,Italy, 1992.
[136] Dorigo M, Stutzle T. Ant Colony Optimization. Cambridge: MIT Press, 2004.
[137] Dorigo M, Stutzle T. The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances. Metaheuristic Network Publication International Summer School on Metaheuristics. 2003.
[138] Christian Blum. Ant colony optimization: Introduction and recent trends. Physics of Life Reviews, 2005, 2 (4): 353–373.
[139]段海滨.蚁群算法原理及其应用.北京:科学出版社, 2005.
[140] Dorigo M, Maniezzo V, Colorni A. Ant System: Optimization by a colony of cooperating agents. IEEE Transaction On System, Man, and Cybernetics- Part B, 1996, 26 (1): 29-41.
[141] B. Bullnheimer, R. R Hard, C. Strauss. A new rank-based version of the Ant System: A computational study. Central European Journal for Operations Research and Economics, 1999, 7 (1): 25-38.
[142] Dorigo M, Gambardella LM. Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Trans Evolutionary, 1997, 1 (1): 53-66.
[143] Stutzle T, Hoos H H. MAX-MIN ant system. Future Generation Computer Systems, 2000, 16 (8): 889-914.
[144] Chi-Bin Cheng, Chun-Pin Mao. A modified ant colony system for solving the travelling salesman problem with time windows. Mathematical and Computer Modelling, 2007, 46 (9-10): 1225-1235.
[145]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, 12 (2): 185-192.
[146]王颖,谢剑英.一种自适应蚁群算法及其仿真研究[J].系统仿真学报.系统仿真学报, 2002, 14 (1): 31-33.
[147]方开泰,马兴长.正交与均匀试验设计.北京:科学出版社, 2001.
[148]任露泉.试验优化设计与分析.北京:高等教育出版社, 2003.
[149]国防科学技术工业委员会.航天发展“十一五”规划.航天器工程, 2008, 17 (1): 1-6.
[2]王永刚,刘玉文.军事卫星及应用概论.北京:国防工业出版社, 2003.
[3]任萱.军事航天技术.北京:国防工业出版社, 1999.
[4]曾华锋,夏洪流,周刚.现代侦察监视技术.北京:国防工业出版社, 2001.
[5]张钧屏,方艾里,万志龙.对地观测与对空监视.北京:科学出版社, 2001.
[6]冯钟葵,石丹,陈文熙,骆艾荣.法国遥感卫星的发展——从SPOT到Pleiades.遥感信息, 2007, 4: 87-92.
[7]孙长喜,魏攀科.国外照相侦察卫星发展综述.国防科技, 2007, 14 (2): 39-41.
[8]梁巍,周润松.国外侦察卫星最新进展.航天器工程, 2007, 16 (2): 30-40.
[9] W. E. STONEY. GUIDE TO LAND IMAGING SATELLITES. http://www.asprs.org, 2008.
[10] E Bensana, G Verfaillie, J C Agnese, N Bataille. Exact and Approximate Methods for the Daily Management of an Earth Observing Satellite. Symposium on Space Mission Operations and Ground Data Systems. Munich, Germany,1996.
[11] Cecile Murat Virginie Gabrel. MP for Earth Observation Satellite Mission Planning. Operations Research in Space and Air,Series: Applied Optimization, 2003.
[12] Dezhen Song, A.Frank, van der Stappen, K. Goldberg. An Exact Algorithm Optimizing Coverage-Resolution for Automated Satellite Frame Selection. Proc of the IEEE International Conference on Robotics and Automation. 2004.63-70.
[13] Wolfe W.J, Sorensen S.E. Three Scheduling Algorithms Applied to the Earth Observing Systems Domain Management Science, 2000, 46 (1): 148-168.
[14] Vasquez M, Hao. J.-K. A "Logic-Constrained" Knapsack Formulation and a Tabu Algorithm for the Daily Photograph Scheduling of an Earth Observation Satellite. Journal of autational Optimization and Applications, 2000.
[15] J Pemberton, Flavius Galiber. A Constraint-Based Approach to Satellite Scheduling. Proceedings of Constraint Programming and Large Scale Discrete Optimization. Piscataway, New Jersey, United States,2001.
[16] J Pemberton. Towards scheduling over-constrained remote sensing satellites. Proceedings of the 2nd International Workshop on Planning and Scheduling for Space. San Francisco, CA,2000.
[17] Nicholas G.Hall, Michael J.Magazine. Maximizing the value of a space mission. European Journal of Operational Research, 1994, 78 (2): 224-241.
[18] Wei-Cheng Lin, Da-Yin Liao, Chung-Yang Liu, Yong-Yao Lee. Daily Imaging Scheduling of an Earth Observation Satellite. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, 2005, 35 (2): 213-223.
[19] Gabrel V, Vanderpooten D. Enumeration and interactive selection of efficient paths in a multiple criteria graph for scheduling an earth observing satellite. European Journal of Operational Research, 2002, 139 (3): 533~542.
[20] Verfaillie G, Bensana E, Edery C.M, Bataillie N. Dealing with Uncertainty When Managing Earth Observing Satellites. Proceedings of i-SAIRAS-99. Netherlands: Noordwijk,1999.
[21] Damiani S, Verfaillie G, Charmeau M C. A Continuous Anytime Planning Module for an Autonomous Earth Watching Satellite. Proc. of the ICAPS-05 Workshop on Planning under Uncertainty for Autonomous Systems. Monterey, CA,2005.
[22] Potter W. A Photo Album of Earth: Scheduling Landsat 7 Mission Daily Activities. Proceedings of the International Symposium Space Mission Operations and Ground Data Systems. Japan,Tokyo,1998.
[23] R Sherwood, A Govindjee, D Yan, G Rabideau. Using ASPEN to Automate EO-1 Activity Planning. Proceedings of the 1998 IEEE Aerospace Conference. Colorado,1998.
[24] R Sherwood, A Govindjee, D Yan. Aspen: EO-1 Mission Activity Planning Made Easy. NASA Workshop on Planning and Scheduling for Space. 1997.
[25] Alex S. Fukunaga, S Chien, D Yan. Aspen: A Framework for Automated Planning and Scheduling of Spacecraft Control and Operations. The Symposium on AI, Robotics and Automation in Space. Tokyo, Japan,1997.
[26] S Chien, G. Rabideau, R. Knig, G. Rabideau. Aspen-Automated Planning and Scheduling for Space Mission Operations. In 6th International Symposium on Space missions Operations and Ground Data Systems. 2000.
[27] R.H. Cohen. Automated Spacecraft Scheduling - the Aster Example. Jet propulsion Laboratory: Ground System Architectures Workshop, 2002.
[28] H. Muraoka, R.H. Cohen, T. Ohno, N. Doi. ASTER Observation Scheduling Algorithm. Proceedings of the 5th International Symposium on Space Mission Operations and Ground Data Systems. Tokyo,Japan,1998.
[29] Alexander F Herz, Adeena Mignogna. Collection Planning for the OrbView-3 High Resolution Imaging Satellite. SpaceOps 2006. Rome,Italy,2006.
[30] Al Globus, James Crawford, Jason Lohn, Robert Morris. Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach. Proceedings of the 3rd International NASA Workshop on Planning and Scheduling for Space. NASA,2002.
[31] Jeremy Frank, Ari Jonsson, Robert Morris, David Smith. Planning andScheduling for Fleets of Earth Observing Satellites. Proceeding of the 6th International Symposium on Artificial Intelligence, Robotics, Automation and Space 2002. Montreal,2002.
[32] Robert A. Morris, Jennifer L. Dungan, John L. Bresina. An Information Infrastructure for Coordinating Earth Science Observations. Proceeding of 2nd IEEE International Conference on Space Mission Challenges for Information Technology. Pasadena, CA,2006.
[33] Morris R, Dungan J, Edgington W, Williams J. Coordinated Science Campaign Scheduling for Sensor Webs. Proceedings of the 8th International Symposium on Artificial Intelligence, Robotics, and Automation in Space. Munchen, Germany,2005.
[34] Jackson Sarah E. Planning Coverage of Points of Interest Via Multiple Imaging Surveillance Assets: A Mulit-Modal Approach. Master's thesis, Air Force Institute of Technology (AFIT), 2003.
[35] Veridian Inc. Generic Resource Event and Activity Scheduler. 2003.
[36] Analytical Graphics Inc. Satellite Tool Kit 5.0. 2003.
[37] Space Imaging Inc. Collection Planning System: A Multi-Source Satellite Imagery Collection Optimization Application. 2003.
[38] P. Mougnaud, L. Galli, C. Castellani, I. Famoso. MAT a Multi-mission Analysis and Planning Tool for Earth Observation Satellite Constellations. The 9th International Conference on Space Operations( SpaceOps 2006). Rome, Italy,2006.
[39] Nicola Bianchessi. Planning and Scheduling Problems for Earth Observation Satellites: Models and Algorithms. Italy: Dipartimento di Tecnologie dell, 2005.
[40] Nicola Bianchessi, Jean-Francois Cordeau, Jacques Desrosiers, Gilbert Laporte. A Heuristic for the Multi-Satellite, Multi-Orbit and Multi-User Management of Earth Observation Satellites. European Journal of Operational Research, 2007, 177 (2): 750-762.
[41] Nicola Bianchessi, Giovanni Righini. Planning and scheduling algorithms for the COSMO-SkyMed constellation. Aerospace Science and Technology, 2008, 12 (7): 535-544.
[42]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.
[43]陈站华.资源卫星任务安排测试方法的研究与实现.无线电工程, 2005, 35 (3): 62-64.
[44]代树武.航天器自主运行关键技术的研究北京:中国科学院空间科学与应用研究中心博士后论文, 2002.
[45]代树武,孙辉先.卫星的智能规划与调度.控制与决策, 2003, 18 (2): 203-206.
[46]刘洋,代树武.卫星有效载荷的规划与调度.华北航天工业学院学报, 2004, 14 (2): 1-4.
[47]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学位论文, 2004.
[48]李菊芳.航天侦察多星多地面站任务规划问题研究.长沙:国防科技大学博士学位论文, 2005.
[49]王均.成像卫星综合任务调度模型与优化方法研究.长沙:国防科技大学博士学位论文, 2007.
[50]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:国防科技大学博士学位论文, 2005.
[51]刘洋.成像侦察卫星动态重调度模型、算法及应用研究.长沙:国防科技大学博士学位论文, 2005.
[52]王军民.成像卫星鲁棒性调度方法及应用研究.长沙:国防科技大学博士学位论文, 2008.
[53] T Beker, G Chechik, I Meilijson, E Ruppin. PlanSat - A Novel Solution for Imaging Satellite Scheduling. White paper. Soligence Corp.
[54] J Dungan, J Frank, A Jonsson, R Morris. Advances in Planning and Scheduling of Remote Sensing Instruments for Fleets of Earth Orbiting Satellites. Proceedings of the 2nd Annual Earth Science Technology Conference. Pasadena, California,2002.
[55] Harrison S A, Price M E. Task Scheduling for Satellite Based Imagery. Proceedings of the Eighteenth Workshop of the UK Planning and Scheduling Special Interest Group. University of Salford, UK, 1999.64-78.
[56] Verfaillie G. Russian Doll Search for Solving Constraint Optimization Problems. Proceedings of AAAI-96. Portland: Oregon,1996.181-187.
[57]徐雪仁,宫鹏,黄学智,金勇.资源卫星(可见光)遥感数据获取任务调度优化算法研究.遥感学报, 2007, 11 (1): 109-114.
[58] John L. Bresina. Heuristic-Biased Stochastic Sampling. Proceedings of the Thirteenth National Conference on Artificial Intelligence. Portland, Oregon,1996.
[59] Angelo Oddi, Amedeo Cesta, Nicola Policella, Gabriella Cortellessa. Scheduling Downlink Operations in MARS-EXPRESS. Proceedings of the 3rd NASA Workshop on Planning and Scheduling. Huston,2002.
[60] Cordeau J-F, Laporte G. Maximizing the Value of an Earth Observation Satellite Orbit. Journal of the Operational Research Society, 2005, 56 (8): 962-968.
[61]李菊芳,谭跃进.卫星观测系统整体调度问题的建模和求解.系统工程理论与实践, 2004, 24 (12): 65-71.
[62] Kuipers EJ. Algorithm for the management of the missions of Earth observation satellites. Fifth ROADEF Annual Conference. Avignon , France,2003.
[63] Parish D A. A Genetic Algorithms Approach to Automating Satellite Range Scheduling. Master’s thesis, air force institute of technology, 1994.
[64] Piero P. Bonissone, Raj Subbu, Neil Eklund, Thomas R. Kiehl. Evolutionary Algorithms + Domain Knowledge = Real-World Evolutionary Computation. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2006, 10 (3): 256-280.
[65] Al. Globus, J. Crawford, J. Lohn, A. Pryor. A Comparison of Techniques for Scheduling Earth Observing Satellites. AAAI. 2004.836-843.
[66] J. Walton. Models for the Management of Satellite-based Sensors. Ph.D. Dissertation, Massachusetts Institute of Technology, 1993.
[67] M. Lema?tre, G. Verfaillie. Daily Management of an Earth Observation Satellite: Comparison of ILOG Solver with Dedicated Algorithms for Valued Constraint Satisfaction Problems. Third ILOG International Users Meeting. Paris,1997.
[68] Michel Lema?tre, Gérard Verfaillie, Frank Jouhaud, Jean-Michel Lachiver. Selecting and Scheduling Observations of Agile Satellites. Aerospace Science and Technology, 2002, 6 (5): 367-381.
[69] Mancel C. Complex Optimization Problems in Space Systems. 13Th International Conference on Automated Planning&Scheduling. Trento,Italy,2003.
[70] Claire Rivett, Carmine Pontecorvo. Improving Satellite Surveillance Through Optimal Assignment of Assets. Australian Government Department of Defence: DSTO-TR-1488, 2004.
[71]李曦.多星区域观测任务的效率优化方法研究.长沙:国防科技大学硕士学位论文, 2005.
[72]祝江汉,李曦,毛赤龙,张利宁.多卫星区域观测任务的侧摆方案优化方法研究.武汉大学学报(信息科学版), 2006, 31 (10): 868-870.
[73]阮启明,谭跃进,李永太,陈英武.基于约束满足的多星对区域目标观测活动协同.宇航学报, 2007, 28 (1): 238-242.
[74]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.
[75]阮启明,谭跃进,李菊芳,陈英武.对地观测卫星的区域目标分割与优选问题研究.测绘科学, 2006, 34 (1): 98-100.
[76] The Landsat Program - WRS. http://landsat.gsfc.nasa.gov/about/wrs.html.
[77] The SPOT Grid Reference System (GRS). http://www.crisp.nus.edu.sg/spot/spot.html.
[78]寇连群.印度ResourceSat-1(P-1)卫星介绍.印度ResourceSat-1(P-1)卫星介绍.中国科学院中国遥感卫星地面站, 2005.
[79] http://www.nspo.org.tw/c60/rocsat2/rs2_property.html.
[80]朱述龙,张占睦.遥感图象获取与分析.北京:科学出版社, 2003.
[81] Short N M. Historical and Technical Perspectives of Remote Sensing,http://rst.gsfc.nasa.gov/. 2004.
[82]文沃根.高分辨率IKONOS卫星影像及其产品的特性.遥感信息, 2001, 9 (1): 37-38.
[83] Gordon Petrie. Monitoring Iraq– Imagery Options for Monitoring and Gathering Intelligence. GI News, 2003: 1-2.
[84]周军.航天器控制原理.西安:西北工业大学出版社, 2001.
[85] Philippe Baptiste, W P M Nuijten. Constraint based scheduling: Applying Constraint Programming to scheduling problems. Kluwer Academic Press, 2001.
[86] E.H.L. Aarts, P.J.M. van Laarhoven, J.K. Lenstra, N.J.L. Ulder. A Computational Study of Local Search Algorithms for Job Shop Scheduling. Journal on Computing, 1994, 6 (2): 118-125.
[87] D Applegate, W Cook. A Computational Study of the Job Shop Scheduling Problem. Journal on Computing, 1991, 3 (3): 149-156.
[88] Torsten Fahle, Ulrich Junker, Stefan E Karisch, Niklas Kohi. Constraint Programming Based Column Generation for Crew Assignment. Journal of Heuristics, 2002, 8 (1): 59-81.
[89] Andersson E, E Houson, N Kohl, D Wedelin. Crew Pairing Optimization, Operational Research in the Airline Industry. International Series in Operational Research and Management Science, 1998, 9: 228-258.
[90] Laura Barbulescu, Adele E. Howe, L. Darrell Whitley, Mark Roberts. Understanding Algorithm Performance on an Oversubscribed Scheduling Application. Journal of Artificial Intelligence Research, 2006, 27: 577-615.
[91] Laura Barbulescu, Adele E. Howe, L. Darrell Whitley, Mark Roberts. Trading Places: How to Schedule More in a Multi-Resource Oversubscribed Scheduling Problem. 2003.
[92]何红艳,乌崇德,王小勇.侧摆对卫星及CCD相机系统参数的影响和分析.航天返回与遥感, 2003, 24 (4): 14-18.
[93]郗晓宁,王威,高玉东.近地航天器轨道基础.长沙:国防科技大学出版社, 2003.
[94]国立中央大学太空及遥测中心. http://www.csrsr.ncu.edu.tw.
[95]胡毓钜,龚剑文,黄伟.地图投影.北京:测绘出版社, 1981.
[96]钱曾波,刘静宇,肖国超.航天摄影测量.北京:解放军出版社, 1992.
[97]朱华统.常用大地坐标系及其变换.北京:解放军出版社, 1990.
[98]朱华统,杨元喜,吕志平. GPS坐标系统的变换.北京:测绘出版社, 1994.
[99] MapInfo Corporation. MapXtreme2005中文开发指南. 2006.
[100] G′erard Verfaillie, Michel Lema??tre. Planning activities for Earth watching and observing satellites and constellations. Proceedings of the Sixteenth International Conference on Automated Planning and Scheduling(ICAPS 2006). Cumbria, UK,2006.
[101] Rojanasoonthon Siwate. Parallel Machine Scheduling with Time Windows. University of Texas,Ph.D Thesis, 2003.
[102]李曙光.批调度与网络问题的组合算法.济南:山东大学博士学位论文, 2007.
[103] C.H.Potts, M.Y.Kovalyov. Scheduling with batching: a review. European Journal of Operational Research, 2000, 120 (2): 228-249.
[104] AZIZOGLU M, WEBSTER S. Scheduling A batch processing machine with incompatible job families. Computers & Industrial Engineering, 2001, 39 (3): 325-335.
[105]王晓峰,谷寒雨.一种改进的半导体生产线批处理机调度策略研究.计算机集成制造系统, 2007, 13 (6): 1115-1120.
[106] DOBSON G, NAMBIMADOM RS. The batch loading and scheduling problem. Operations Rosearch, 2001, 49 (1): 52-65.
[107] MATHIRAJAN M, IYER S A, CHANDRU V. Scheduling algorithms and sensitivity analysis for heterogeneous batch processors with incompatible job families. Journal of the Chinese lnstitute of lndustrial Engineers, 2004, 21 (1): 18-26.
[108] MATHIRAJAN M, SlVAKUMAR A L. Minimizing total weighted tardiness on heterogeneous batch Processing machine with incompatible job families. The lnternational Jourual of Advanced Manufacturing Technology, 2006, 21 (9): 1038-1047.
[109]金锋,宋士吉,杨建华,吴澄.染整车间染缸优化调度算法研究.计算机集成制造系统, 2008, 14 (3): 543-547.
[110] DEVPURA A, FOWLER J W, CARLYLE MW. Minimizing total weighted tardiness on single batch process machine with incompatible job families. Proceedings of the Symposiym on Operations Research 2000. Berlin,Germany,Springer-Verlag, 2000.366-371.
[111]王振国,陈小前,罗文彩,张卫华等.飞行器多学科设计优化理论与应用研究.北京:国防工业出版社, 2006.
[112]尚文利.车间计划调度集成模型研究.沈阳:中国科学院沈阳自动化研究所博士学位论文, 2005.
[113]王凌,郑大钟.邻域搜索算法的统一结构和混合优化策略.清华大学学报(自然科学版), 2000, 40 (9): 125-128.
[114]玄光南,程润伟.遗传算法与工程优化.北京:清华大学出版社, 2004.
[115] Lau, Hoong Chuin, Melvyn Sim, Kwong Meng Teo. Vehicle Routing Problem With Time Windows and a Limited Number of Vehicles. European Journal of Operational Research, 2003, 148 559-569.
[116] ILOG. ILOG Dispatcher 4.0 User's Manual. 2003.
[117] Metropolis N, Rosenbluth A, Rosenbluth M. Equation of state calculations by fast computing machines. Journal of Chemical Physics, 1953, 21: 1087-1092.
[118] Kirkpatrick S, Gelatt Jr C D, Vecchi M P. Optimization by simulated annealing. Science, 1983, 220: 671-680.
[119]邢文训,谢金星.现代优化计算方法.北京:清华大学出版社, 2005.
[120] B Suman, P Kumar. A survey of simulated annealing as a tool for single and multiobjective optimization. Journal of the Operational Research Society, 2006, 57 (10): 1143-1160.
[121]陈华根,吴健生,王家林,陈冰.模拟退火算法机理研究.同济大学学报(自然科学版), 2004, 32 (6): 802-805.
[122] Ingber L. Very Fast Simulated Re-annealing. Math Compute Modelling, 1989, 12: 967-973.
[123] Sudarshan Banerjee, Nikil Dutt. R. Very fast Simulated Annealing for HW-SW partitioning. Technical Report ,CECS-TR-04-17. UC,Irvine,2004.
[124]陈华根,李丽华,许惠平,陈冰.改进的非常快速模拟退火算法.同济大学学报(自然科学版), 2006, 8 (34): 1121-1125.
[125] Siwate Rojanasoonthon, Jonathan Bard. A GRASP for Parallel Machine Scheduling with Time Windows. INFORMS Journal on Computing, 2005, 17 (1): 32-52.
[126]李建,张永.一类集散货物路线问题的禁忌搜索算法设计.系统工程理论与实践, 2007, 27 (6): 117-123.
[127] Ali Manzak, Huseyin Goksu. Application of Very Fast Simulated Reannealing (VFSR) to Low Power Design. Embedded Computer Systems: Architectures, Modeling, and Simulation. Berlin Heidelberg,Springer-Verlag 2005.308-313.
[128] A. Anagnostopoulos, L. Michel, P. Van Hentenryck, Y. Vergados. A Simulated Annealing Approach to the Traveling Tournament Problem. Journal of Scheduling, 2006, 6 (2): 177-193.
[129] M.M.Solomon. Algorithms for Vehicle Routing and Scheduling Problems with Time Window Constraints. Operations Reasearch, 1987, 35 (2): 254-265.
[130] Galvao R D, Espejo L G A, Boffey B. A comparison of Lagrangean and surrogaterelaxations for the maximal covering location problem. European Journal of Operational Research, 2000, 24 (1): 377-389.
[131] Schilling D A, Vaidyanathan J, Barkhi L R. A review of covering problems in facility location. Location Science, 1993, 1: 25-55.
[132] Megiddo N, Zemel E, Hakimi S L. The maximum coverage location problem. SIAM Journal on Algebraic and Discrete Methods, 1983, 4 (2): 253-261.
[133] Daskin M S. Network and discrete location: models algorithms and applications. Network and discrete location: models algorithms and applications. NewYork: Wiley Interscience, 1995.
[134] Colorni A, Dorigo M, Maniezzo V. Distributed Optimization by Ant Colonies. Proceedings of European Conference on Artificial Life. France,1991.
[135] Dorigo M. Optimization, learning and natural algorithms. PhD thesis,Dipartimento di Elettronica, Politecnico di Milano,Italy, 1992.
[136] Dorigo M, Stutzle T. Ant Colony Optimization. Cambridge: MIT Press, 2004.
[137] Dorigo M, Stutzle T. The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances. Metaheuristic Network Publication International Summer School on Metaheuristics. 2003.
[138] Christian Blum. Ant colony optimization: Introduction and recent trends. Physics of Life Reviews, 2005, 2 (4): 353–373.
[139]段海滨.蚁群算法原理及其应用.北京:科学出版社, 2005.
[140] Dorigo M, Maniezzo V, Colorni A. Ant System: Optimization by a colony of cooperating agents. IEEE Transaction On System, Man, and Cybernetics- Part B, 1996, 26 (1): 29-41.
[141] B. Bullnheimer, R. R Hard, C. Strauss. A new rank-based version of the Ant System: A computational study. Central European Journal for Operations Research and Economics, 1999, 7 (1): 25-38.
[142] Dorigo M, Gambardella LM. Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Trans Evolutionary, 1997, 1 (1): 53-66.
[143] Stutzle T, Hoos H H. MAX-MIN ant system. Future Generation Computer Systems, 2000, 16 (8): 889-914.
[144] Chi-Bin Cheng, Chun-Pin Mao. A modified ant colony system for solving the travelling salesman problem with time windows. Mathematical and Computer Modelling, 2007, 46 (9-10): 1225-1235.
[145]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, 12 (2): 185-192.
[146]王颖,谢剑英.一种自适应蚁群算法及其仿真研究[J].系统仿真学报.系统仿真学报, 2002, 14 (1): 31-33.
[147]方开泰,马兴长.正交与均匀试验设计.北京:科学出版社, 2001.
[148]任露泉.试验优化设计与分析.北京:高等教育出版社, 2003.
[149]国防科学技术工业委员会.航天发展“十一五”规划.航天器工程, 2008, 17 (1): 1-6.