Abstract
This paper addresses the integrated Earth observation satellite scheduling problem. It is a complicated problem because observing and downloading operations are both involved. We use an acyclic directed graph model to describe the observing and downloading integrated scheduling problem. Based on the model which considering energy constraints and storage capacity constraints, we develop an efficient solving method using a novel quantum genetic algorithm. We design a new encoding and decoding scheme that can generate feasible solution and increase the diversity of the population. The results of the simulation experiments show that the proposed method solves the integrated Earth observation satellite scheduling problem with good performance and outperforms the genetic algorithm and greedy algorithm on all instances.
References
[1] Globus A, Crawford J, Lohn J, et al. A comparison of techniques for scheduling earth observing satellites. National Conference on Artifical Intelligence AAAI Press, 2004: 836–843.Suche in Google Scholar
[2] Lemaitre M, Verfaillie G, Jouhaud F, et al. Selecting and scheduling observations of agile satellites. Aerospace Science and Technology, 2002, 6(5): 367–381.10.1016/S1270-9638(02)01173-2Suche in Google Scholar
[3] Hall N G, Magazine M J. Maximizing the value of a space mission. European Journal of Operational Research, 1994, 78(2): 224–241.10.1016/0377-2217(94)90385-9Suche in Google Scholar
[4] Schiex T. Russian doll search for solving constraint optimization problems. Thirteenth National Conference on Artificial Intelligence AAAI Press, 1996: 181–187.Suche in Google Scholar
[5] Wolfe W J, Sorensen S E. Three scheduling algorithms applied to the Earth observing systems domain. INFORMS, 2000.10.1287/mnsc.46.1.148.15134Suche in Google Scholar
[6] 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. Annals of Operations Research, 1997, 69(1): 115–134.10.1023/A:1018920709696Suche in Google Scholar
[7] Wolfe W J, Sorensen S E. Heuristics for scheduling Earth observing satellites. Proc SPIE, 1999, 3750: 328–339.10.1117/12.363528Suche in Google Scholar
[8] Han S M, Beak S W, Cho K R, et al. Satellite mission scheduling using genetic algorithm. SICE Conference IEEE, 2008: 1226–1230.Suche in Google Scholar
[9] Feng Y. Design and implementation of multi-source satellite imagery collection scheduling system. Spacecraft Recovery and Remote Sensing, 2003.Suche in Google Scholar
[10] Tatem A J, Goetz S J, Hay S I. Fifty years of Earth-observation satellites. American Scientist, 2008, 96(5): 390–398.10.1511/2008.74.390Suche in Google Scholar PubMed
[11] Bensana E, Lemaitre M, Verfaillie G. Earth observation satellite management. Constraints, 1999, 4(3): 293–299.10.1023/A:1026488509554Suche in Google Scholar
[12] Gabrel V. Strengthened 0-1 linear formulation for the daily satellite mission planning. Journal of Combinatorial Optimization, 2006, 11(3): 341–346.10.1007/s10878-006-7912-4Suche in Google Scholar
[13] 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): 533–542.10.1016/S0377-2217(01)00188-6Suche in Google Scholar
[14] Mansour M A A, Dessouky M M. A genetic algorithm approach for solving the daily photograph selection problem of the SPOT5 satellite. Computers and Industrial Engineering, 2010, 58(3): 509–520.10.1016/j.cie.2009.11.012Suche in Google Scholar
[15] Bianchessi N, Cordeau J F, Desrosiers J, et al. A heuristic for the multi-satellite, multi-orbit and multiuser management of Earth observation satellites. European Journal of Operational Research, 2007, 177(2): 750–762.10.1016/j.ejor.2005.12.026Suche in Google Scholar
[16] Tangpattanakul P, Jozefowiez N, Lopez P. Biased random key genetic algorithm for multi-user earth observation scheduling. Recent Advances in Computational Optimization. Springer International Publishing, 2015: 143–160.10.1007/978-3-319-12631-9_9Suche in Google Scholar
[17] Tangpattanakul P, Jozefowiez N, Lopez P. A multi-objective local search heuristic for scheduling Earth observations taken by an agile satellite. European Journal of Operational Research, 2015, 245(2): 542–554.10.1016/j.ejor.2015.03.011Suche in Google Scholar
[18] Bianchessi N, Righini G. Planning and scheduling algorithms for the COSMO-SkyMed constellation. Aerospace Science and Technology, 2008, 12(7): 535–544.10.1016/j.ast.2008.01.001Suche in Google Scholar
[19] Wang P, Reinelt G, Gao P, et al. A model, a heuristic and a decision support system to solve the scheduling problem of an earth observing satellite constellation. Computers and Industrial Engineering, 2011, 61(2): 322–335.10.1016/j.cie.2011.02.015Suche in Google Scholar
[20] Liu X, Bai B, Chen Y, et al. Multi satellites scheduling algorithm based on task merging mechanism. Applied Mathematics and Computation, 2014, 230(2): 687–700.10.1016/j.amc.2013.12.109Suche in Google Scholar
[21] Globus A, Crawford J, Lohn J, et al. Scheduling Earth observing fleets using evolutionary algorithms: Problem description and approach. Proceedings of International Nasa Workshop on Planning and Scheduling for Space, 2002: 27–29.Suche in Google Scholar
[22] Chen Y, Tan Y, He R, et al. Multi-satellite mission planning for integrated space and ground system. 2006.Suche in Google Scholar
[23] Chen Y, Zhang D, Zhou M, et al. Multi-satellite observation scheduling algorithm based on hybrid genetic particle swarm optimization. Springer Berlin Heidelberg, 2012: 441–448.10.1007/978-3-642-26001-8_58Suche in Google Scholar
[24] Wu G, Liu J, Ma M, et al. A two-phase scheduling method with the consideration of task clustering for earth observing satellites. Computers and Operations Research, 2013, 40(7): 1884–1894.10.1016/j.cor.2013.02.009Suche in Google Scholar
[25] Sarkheyli A, Bagheri A, Ghorbani-Vaghei B, et al. Using an effective tabu search in interactive resources scheduling problem for LEO satellites missions. Aerospace Science and Technology, 2013, 29(1): 287–295.10.1016/j.ast.2013.04.001Suche in Google Scholar
[26] Spangelo S, Cutler J, Gilson K, et al. Optimization-based scheduling for the single-satellite, multi-ground station communication problem. Computers and Operations Research, 2015, 57(C): 1–16.10.1016/j.cor.2014.11.004Suche in Google Scholar
[27] Xu R, Chen H, Liang X, et al. Priority-based constructive algorithms for scheduling agile earth observation satellites with total priority maximization. Expert Systems with Applications, 2016, 51(C): 195–206.10.1016/j.eswa.2015.12.039Suche in Google Scholar
[28] Garey M R, Johnson D S. Computers and intractability: A guide to the theory of NP-completeness. W H Freeman, 1986.Suche in Google Scholar
[29] Hansen P. Bicriterion path problems. Lecture Notes in Economics and Mathematical Systems, 1980, 177: 109–127.10.1007/978-3-642-48782-8_9Suche in Google Scholar
[30] Zhang G, Li N, Jin W, et al. Novel quantum genetic algorithm and its applications. Frontiers of Electrical and Electronic Engineering in China, 2006, 1(1): 31–36.10.1007/s11460-005-0014-8Suche in Google Scholar
[31] Han K H, Kim J H. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Transactions on Evolutionary Computation, 2002, 6(6): 580–593.10.1109/TEVC.2002.804320Suche in Google Scholar
[32] Grover, Lov K. A fast quantum mechanical algorithm for database search. Annual ACM Symposium on Theory of Computing, 1996: 212–219.10.1145/237814.237866Suche in Google Scholar
[33] Wang Y P, Li Y H. A novel quantum genetic algorithm for TSP. Chinese Journal of Computers, 2007(5): 748–755.10.1109/ICMLC.2007.4370274Suche in Google Scholar
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Artikel in diesem Heft
- Co-evolution: A New Perspective for Business Model Innovation
- Solving the Observing and Downloading Integrated Scheduling Problem of Earth Observation Satellite with a Quantum Genetic Algorithm
- A Modified Gravity p-Median Model for Optimizing Facility Locations
- Real-Time Pricing for Smart Grid with Multiple Companies and Multiple Users Using Two-Stage Optimization
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- Finding Cut-Edges and the Minimum Spanning Tree via Semi-Tensor Product Approach
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