Document Open Access Logo

Earth Observation Satellite Constellation Planning with Matheuristics and Metaheuristics Combination

Authors Romain Barrault , Cedric Pralet , Gauthier Picard , Eric Sawyer

PDF

File

Thumbnail PDF
PDF
OASIcs.TIME.2026.12.pdf
  • Filesize: 1.59 MB
  • 17 pages

Document Identifiers

Subject Classification

ACM Subject Classification
  • Computing methodologies → Planning and scheduling
  • Applied computing → Operations research
  • Computing methodologies → Combinatorial algorithms
  • Mathematics of computing → Combinatorial optimization
Keywords
  • Scheduling
  • Earth Observation Satellite
  • Matheuristic
  • Metaheuristic
  • Linear Programming

Metrics

Abstract

A standard problem in the field of Earth observation is the scheduling of the observations of an agile satellite constellation. Given a set of end-user requests over Points Of Interest (POIs), it consists in selecting observations among the candidate ones, attributing each of them to a satellite, and defining the sequence of observations planned for each satellite given operational constraints. The latter are related to the visibility windows available to observe the POIs and the time-dependent maneuvers required to reorient the observation instrument between two POIs. They result in a highly combinatorial problem that must be solved in a restricted amount of time. To solve such a complex problem, we propose an approach that combines matheuristics to filter the observation tasks and metaheuristics to schedule them. Firstly, we solve a Sequential Ordering Problem for each satellite to get a giant tour visiting all the visible POIs. From this giant tour, we exploit a Linear Programming Model to compute the best set of observations under several tour length constraints. Finally, we schedule the selected observations based on a Large Neighborhood Search. This three-step method notoriously improves the solution quality when compared to a baseline scheduling approach.

Cite As Get BibTex

Romain Barrault, Cedric Pralet, Gauthier Picard, and Eric Sawyer. Earth Observation Satellite Constellation Planning with Matheuristics and Metaheuristics Combination. In 33rd International Symposium on Temporal Representation and Reasoning (TIME 2026). Open Access Series in Informatics (OASIcs), Volume 146, pp. 12:1-12:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/OASIcs.TIME.2026.12

Author Details

Romain Barrault
  • DTIS, ONERA, Université de Toulouse, 31000, Toulouse, France
Cedric Pralet
  • DTIS, ONERA, Université de Toulouse, 31000, Toulouse, France
Gauthier Picard
  • DTIS, ONERA, Université de Toulouse, 31000, Toulouse, France
Eric Sawyer
  • CNES, Toulouse, France

References

  1. Youcef Amarouche, Rym Nesrine Guibadj, Elhadja Chaalal, and Aziz Moukrim. Effective neighborhood search with optimal splitting and adaptive memory for the team orienteering problem with time windows. Computers & Operations Research, 123:105039, 2020. URL: https://doi.org/10.1016/j.cor.2020.105039.
  2. Valentin Antuori, Damien T. Wojtowicz, and Emmanuel Hebrard. Solving the Agile Earth Observation Satellite Scheduling Problem with CP and Local Search. In Maria Garcia de la Banda, editor, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025), volume 340 of Leibniz International Proceedings in Informatics (LIPIcs), pages 3:1-3:22, Dagstuhl, Germany, 2025. Schloss Dagstuhl - Leibniz-Zentrum für Informatik. URL: https://doi.org/10.4230/LIPIcs.CP.2025.3.
  3. Romain Barrault, Cédric Pralet, Gauthier Picard, and Eric Sawyer. Hybridizing machine learning and optimization for planning satellite observations. In Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 22nd International Conference, CPAIOR 2025, Melbourne, VIC, Australia, November 10–13, 2025, Proceedings, Part I, pages 68-85, Berlin, Heidelberg, 2025. Springer-Verlag. URL: https://doi.org/10.1007/978-3-031-95973-8_5.
  4. Marco Antonio Boschetti and Vittorio Maniezzo. Matheuristics: using mathematics for heuristic design. 4OR, 20(2):173-208, 2022. URL: https://doi.org/10.1007/S10288-022-00510-8.
  5. Hermann Bouly, Duc-Cuong Dang, and Aziz Moukrim. A memetic algorithm for the team orienteering problem. In Mario Giacobini, Anthony Brabazon, Stefano Cagnoni, Gianni A. Di Caro, Rolf Drechsler, Anikó Ekárt, Anna Isabel Esparcia-Alcázar, Muddassar Farooq, Andreas Fink, Jon McCormack, Michael O'Neill, Juan Romero, Franz Rothlauf, Giovanni Squillero, A. Şima Uyar, and Shengxiang Yang, editors, Applications of Evolutionary Computing, pages 649-658, Berlin, Heidelberg, 2008. Springer Berlin Heidelberg. URL: https://doi.org/10.1007/978-3-540-78761-7_71.
  6. Diego Cattaruzza, Nabil Absi, and Dominique Feillet. The multi-trip vehicle routing problem with time windows and release dates. Transportation Science, 50(2):676-693, 2016. URL: https://doi.org/10.1287/TRSC.2015.0608.
  7. Xiaoyu Chen, Gerhard Reinelt, Guangming Dai, and Andreas Spitz. A mixed integer linear programming model for multi-satellite scheduling. European Journal of Operational Research, 275(2):694-707, 2019. URL: https://doi.org/10.1016/j.ejor.2018.11.058.
  8. W. Dullaert, G.K. Janssens, K. Sirensen, and B. Vernimmen. New heuristics for the fleet size and mix vehicle routing problem with time windows. Journal of the Operational Research Society, 53(11):1232-1238, 2002. Cited by: 49. URL: https://doi.org/10.1057/palgrave.jors.2601422.
  9. Romain Fontaine, Jilles Dibangoye, and Christine Solnon. Exact and anytime approach for solving the time dependent traveling salesman problem with time windows. European Journal of Operational Research, 311(3):833-844, 2023. URL: https://doi.org/10.1016/j.ejor.2023.06.001.
  10. Ander Garcia, Pieter Vansteenwegen, Olatz Arbelaitz, Wouter Souffriau, and Maria Teresa Linaza. Integrating public transportation in personalised electronic tourist guides. Computers & Operations Research, 40(3):758-774, 2013. Transport Scheduling. URL: https://doi.org/10.1016/j.cor.2011.03.020.
  11. Damianos Gavalas, Charalampos Konstantopoulos, Konstantinos Mastakas, Grammati Pantziou, and Nikolaos Vathis. Efficient heuristics for the time dependent team orienteering problem with time windows. In Prosenjit Gupta and Christos Zaroliagis, editors, Applied Algorithms, pages 152-163, Cham, 2014. Springer International Publishing. URL: https://doi.org/10.1007/978-3-319-04126-1_13.
  12. Al Globus, James Crawford, Jason Lohn, and Anna Pryor. A comparison of techniques for scheduling earth observing satellites. In Proceedings of the 16th Conference on Innovative Applications of Artifical Intelligence, IAAI'04, pages 836-843. AAAI Press, 2004. Google Scholar
  13. Keld Helsgaun. An Extension of the Lin-Kernighan-Helsgaun TSP Solver for Constrained Traveling Salesman and Vehicle Routing Problems, December 2017. URL: https://doi.org/10.13140/RG.2.2.25569.40807.
  14. M. Khodadadian, A. Divsalar, C. Verbeeck, A. Gunawan, and P. Vansteenwegen. Time dependent orienteering problem with time windows and service time dependent profits. Computers & Operations Research, 143:105794, 2022. URL: https://doi.org/10.1016/j.cor.2022.105794.
  15. Minkeon Lee, Seunghyeon Yu, Kybeom Kwon, Myungshin Lee, Junghyun Lee, and Heungseob Kim. Mixed-integer linear programming model for scheduling missions and communications of multiple satellites. Aerospace, 11(1), 2024. URL: https://doi.org/10.3390/aerospace11010083.
  16. Bohua Li, Ming Chen, Lining Xing, Yingguo Chen, and Yingwu Chen. Optimizing time-dependent multi-agile earth observation satellite scheduling problem using deep q-learning and ensemble heuristics. Information Sciences, 712:122140, 2025. URL: https://doi.org/10.1016/j.ins.2025.122140.
  17. Xiaolu Liu, Gilbert Laporte, Yingwu Chen, and Renjie He. An adaptive large neighborhood search metaheuristic for agile satellite scheduling with time-dependent transition time. Computers and Operations Research, 86:41-53, 2017. Cited by: 246. URL: https://doi.org/10.1016/j.cor.2017.04.006.
  18. David Pisinger and Stefan Ropke. Large neighborhood search. In Michel Gendreau and Jean-Yves Potvin, editors, Handbook of Metaheuristics, pages 99-127. Springer International Publishing, Cham, 2019. URL: https://doi.org/10.1007/978-3-319-91086-4_4.
  19. Cédric Pralet. Iterated maximum large neighborhood search for the traveling salesman problem with time windows and its time-dependent version. Computers & Operations Research, 150:106078, 2023. URL: https://doi.org/10.1016/j.cor.2022.106078.
  20. Lili Ren, Xin Ning, and Zheng Wang. A competitive markov decision process model and a recursive reinforcement-learning algorithm for fairness scheduling of agile satellites. Computers and Industrial Engineering, 169, 2022. Cited by: 28. URL: https://doi.org/10.1016/j.cie.2022.108242.
  21. Vincent F. Yu, Parida Jewpanya, Shih-Wei Lin, and A.A.N. Perwira Redi. Team orienteering problem with time windows and time-dependent scores. Computers & Industrial Engineering, 127:213-224, 2019. URL: https://doi.org/10.1016/j.cie.2018.11.044.
Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact