Abstract
The United States Naval Research Laboratory is currently using permutation-based genetic algorithms for large-scale satellite resource scheduling. This is a real-world, deployed application. The permutations must be mapped to a Gantt chart representing the final schedule. How this mapping is done can have a significant impact on the ability of the search algorithm to discover high-quality solutions. We present new work that uses compaction strategies in combination with genetic algorithms to construct less fragmented schedules. A schedule with “fewer holes” should also translate into better resource utilization. We show that this is indeed the case. This work is impactful because this strategy can be used to improve all genetic algorithm schedulers .
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References
Barbulescu, L., Watson, J., Whitley, D., Howe, A.: Scheduling space–ground communications for the air force satellite control network. J. Sched. 7(1), 7–34 (2004). https://doi.org/10.1023/B:JOSH.0000013053.32600.3c
Barbulescu, L., Howe, A.E., Whitley, L.D., Roberts, M.: Understanding algorithm performance on an oversubscribed scheduling application. J. Artif. Intell. Res. 27, 577–615 (2006)
Barbulescu, L., Whitley, L.D., Howe, A.E.: Leap before you look: an effective strategy in an oversubscribed scheduling problem. In: Proceedings of the 19th conference on Artificial Intelligence, pp. 143–148 (2004)
Chen, H., Luo, Z., Peng, S., Wu, J., Li, J.: HiPGen: an approach for fast generation of multi-satellite observation plans via a hierarchical multi-channel transformer network. Adv. Space Res. 69(8), 3103–3116 (2022). https://doi.org/10.1016/j.asr.2022.01.037
Chen, X., Reinelt, G., Dai, G., Spitz, A.: A mixed integer linear programming model for multi-satellite scheduling. Eur. J. Oper. Res. 275(2), 694–707 (2019). https://doi.org/10.1016/j.ejor.2018.11.058
Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)
Davis, L.: Job shop scheduling with genetic algorithms. In: Grefenstette, J. (ed.) Int’l. Conf. on GAs and Their Applications, pp. 136–140 (1985)
Goh, E., Venkataram, H.S., Hoffmann, M., Johnston, M.D., Wilson, B.: Scheduling the NASA deep space network with deep reinforcement learning. In: 2021 IEEE Aerospace Conference (50100), pp. 1–10. IEEE, Big Sky, MT, USA (Mar 2021). https://doi.org/10.1109/AERO50100.2021.9438519, https://ieeexplore.ieee.org/document/9438519/
Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA (1989)
Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)
Kolici, V., Herrero, X., Xhafa, F., Barolli, L.: Local search and genetic algorithms for satellite scheduling problems. In: 2013 Eighth International Conference on Broadband and Wireless Computing, Communication and Applications, pp. 328–335. IEEE, Compiegne, France (Oct 2013). https://doi.org/10.1109/BWCCA.2013.58, http://ieeexplore.ieee.org/document/6690906/
Li, S., Yu, Q., Ding, H.: Reviews and prospects in satellite range scheduling problem. Auton. Intell. Syst. 3(1), 9 (2023). https://doi.org/10.1007/s43684-023-00054-6
Linares, L., Vazquez, R., Perea, F., Galán-Vioque, J.: A mixed integer linear programming model for resolution of the antenna-satellite scheduling problem. In: IEEE Transactions on Aerospace and Electronic Systems, pp. 1–13 (2023). https://doi.org/10.1109/TAES.2023.3326422
Liu, Q., Li, S., Zhang, P., Liu, F.: Research on satellite communication resource scheduling method based on adaptive genetic algorithm. In: 2023 4th International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), pp. 382–388. IEEE, Nanjing, China (Aug 2023). https://doi.org/10.1109/ISCEIC59030.2023.10271164, https://ieeexplore.ieee.org/document/10271164/
Liu, Z., Feng, Z., Ren, Z.: Route-reduction-based dynamic programming for large-scale satellite range scheduling problem. Eng. Optim. 51(11), 1944–1964 (2019). https://doi.org/10.1080/0305215X.2018.1558445
Niu, X., Tang, H., Wu, L.: Satellite scheduling of large areal tasks for rapid response to natural disaster using a multi-objective genetic algorithm. Int. J. Disaster Risk Reduction 28, 813–825 (2018). https://doi.org/10.1016/j.ijdrr.2018.02.013
Peng, G., Song, G., Xing, L., Gunawan, A., Vansteenwegen, P.: An exact algorithm for agile earth observation satellite scheduling with time-dependent profits. Comput. Oper. Res. 120, 104946 (2020). https://doi.org/10.1016/j.cor.2020.104946
Radcliffe, N., Surry, P.: Fitness variance of formae and performance predictions. In: Whitley, D., Vose, M. (eds.) FOGA - 3, pp. 51–72. Morgan Kaufmann (1995)
Stottler, R., Richards, R.: Managed intelligent deconfliction and scheduling for satellite communication. In: 2018 IEEE Aerospace Conference, pp. 1–7. IEEE, Big Sky, MT (Mar 2018). https://doi.org/10.1109/AERO.2018.8396654, https://ieeexplore.ieee.org/document/8396654/
Syswerda, G.: Schedule optimization using genetic algorithms. In: Davis, L. (ed.) Handbook of Genetic Algorithms, pp. 332–349. Van Nostrand Reinhold, New York, NY (January (1991)
Syswerda, G., Palmucci, J.: The application of genetic algorithms to resource scheduling. In: Booker, L., Belew, R. (eds.) Proc. of the 4th Int’l. Conf. on GAs. Morgan Kaufmann (1991)
Tormos, P.., Lova, A.., Barber, F.., Ingolotti, L.., Abril, M.., Salido, M.. A..: A genetic algorithm for railway scheduling problems. In: Xhafa, F., Abraham, A. (eds.) Metaheuristics for scheduling in industrial and manufacturing applications, pp. 255–276. Springer Berlin Heidelberg, Berlin, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78985-7_10
Whitley, D., Quevedo De Carvalho, O., Roberts, M., Shetty, V., Jampathom, P.: Scheduling multi-resource satellites using genetic algorithms and permutation based representations. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1473–1481. ACM (2023)
Whitley, D., Starkweather, T., Fuquay, D.: Scheduling problems and traveling salesmen: the genetic edge recombination operator. In: Schaffer, J.D. (ed.) Proc. of the 3rd Int’l. Conf. on GAs. Morgan Kaufmann (1989)
Whitley, L.D., Starkweather, T., Shaner, D.: The traveling salesman and sequence scheduling: quality solutions using genetic edge recombination. In: Davis, L. (ed.) Handbook of Genetic Algorithms, chap. 22, pp. 350–372. Van Nostrand Reinhold, New York (1991)
Xu, Y., Liu, X., He, R., Chen, Y.: Multi-satellite scheduling framework and algorithm for very large area observation. Acta Astronaut. 167, 93–107 (2020). https://doi.org/10.1016/j.actaastro.2019.10.041
Zhang, J., Xing, L.: An improved genetic algorithm for the integrated satellite imaging and data transmission scheduling problem. Comput. Oper. Res. 139, 105626 (2022). https://doi.org/10.1016/j.cor.2021.105626
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Whitley, D., de Carvalho, O.Q., Roberts, M., Shetty, V., Jampathom, P. (2024). Satellite Resource Scheduling: Compaction Strategies for Genetic Algorithm Schedulers. In: Affenzeller, M., et al. Parallel Problem Solving from Nature – PPSN XVIII. PPSN 2024. Lecture Notes in Computer Science, vol 15151. Springer, Cham. https://doi.org/10.1007/978-3-031-70085-9_21
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