A Mixed Integer Linear Programming Model for Resolution of the Antenna-Satellite Scheduling Problem

This article deals with one of the types of “Satellite Range Scheduling” problems arising in Earth Observation Satellite operations, Antenna-Satellite Scheduling. Given a set of satellites, a set of available antennas and a time horizon, the problem consists of designing an operational plan that ass...

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Veröffentlicht in:IEEE transactions on aerospace and electronic systems Jg. 60; H. 1; S. 463 - 473
Hauptverfasser: Linares, Lorena, Vazquez, Rafael, Perea, Federico, Galan-Vioque, Jorge
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Antenna-satellite allocation
Antennas
Computational modeling
Continuity (mathematics)
deconfliction
Earth observations (from space)
Genetic algorithms
Integer programming
Linear programming
Mixed integer
mixed integer linear programming
optimal satellite scheduling
Optimal scheduling
Satellite antennas
Satellite observation
satellite range scheduling
Satellites
Scheduling
Shaving
Task analysis
ISSN:0018-9251, 1557-9603
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Zusammenfassung:This article deals with one of the types of “Satellite Range Scheduling” problems arising in Earth Observation Satellite operations, Antenna-Satellite Scheduling. Given a set of satellites, a set of available antennas and a time horizon, the problem consists of designing an operational plan that assigns satellites to antennas in an optimal fashion. Extending a previous integer linear programming (ILP) model (shortening model, with only integer variables), we propose a mixed ILP (MILP) (shaving model, which includes both continuous and integer variables), to more efficiently solve this problem. After computing the passes generated by the satellites' windows of visibility from the antennas, the optimal planner is able to cancel a pass, move it to another antenna, or shorten its duration, in order to avoid scheduling conflicts. In contrast to the shortening model, which used intersections between passes to determine the best schedule, the shortening operation is now referred to as shaving, since the shaving model can arbitrarily adjust the duration of a pass in a razor-like fashion, giving the model its name. Computational results obtained in tests over realistic scenarios prove that the shaving model outperforms the shortening model, producing fewer cancellations, smaller shaved times, and a fairer distribution of cancelled passes among satellites, with much shorter preprocessing times.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2023.3326422