Tabu-Based Large Neighbourhood Search for Time-Dependent Multi-Orbit Agile Satellite Scheduling

Tabu-Based Large Neighbourhood Search for Time-Dependent Multi-Orbit Agile Satellite Scheduling

He, L. (TU Delft Algorithmics; National University of Defense Technology)
de Weerdt, M.M. (TU Delft Algorithmics)
Yorke-Smith, N. (TU Delft Algorithmics)
Liu, Xiaolu (National University of Defense Technology)
Chen, Yingwu (National University of Defense Technology)

Bernardini, S. (editor)
Parkinson, S, (editor)
Talamadupula, K. (editor)

2018

Agile Earth observation satellite (AEOS) scheduling is complex, due to long visible time windows and time-dependent transitions between observations. We introduce a generic approach suited for scheduling problems characterised by time-dependency and/or sequence-dependency. Our approach is a novel hybridization of adaptive large neighbourhood search (ALNS) and tabu search. We further introduce partial sequence dominance and insertion position ordering operators to the ALNS. Extensive computational results on a real-world multi-orbit AEOS observation scheduling benchmark show that the hybrid ALNS robustly outperforms an improved mixed integer programming model and two recent state-of-the-art metaheuristic methods. The proposed method increases solution quality by more than 10% and reduces calculation time by more than 70% on average

http://resolver.tudelft.nl/uuid:28c63d14-06fa-431c-9d91-b43fd435831d

2019-07-01

SPARK 2018: Proceedings of the 11th International Workshop on Scheduling and Planning Applications (SPARK)

SPARK 2018, 2019-06-24 → 2019-06-29, Delft, Netherlands

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2018 L. He, M.M. de Weerdt, N. Yorke-Smith, Xiaolu Liu, Yingwu Chen