A mixed integer linear programming model for multi-satellite scheduling

•We propose an exact mixed integer linear program for agile satellites scheduling.•The model includes time window distributions and observation resource capacities.•Conflict indicators of visible time windows constitute central decision variables.•Using 5-index variables obviates the need for a Big-...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:European journal of operational research Ročník 275; číslo 2; s. 694 - 707
Hlavní autori: Chen, Xiaoyu, Reinelt, Gerhard, Dai, Guangming, Spitz, Andreas
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.06.2019
Predmet:
Earth observing satellites
Integer programming
Mathematical programming
Scheduling
Earth observing satellites
Integer programming
Scheduling
Mathematical programming
ISSN:0377-2217, 1872-6860
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:•We propose an exact mixed integer linear program for agile satellites scheduling.•The model includes time window distributions and observation resource capacities.•Conflict indicators of visible time windows constitute central decision variables.•Using 5-index variables obviates the need for a Big-M approach.•We feasibly reach an optimality gap of less than 2.5% on all test instances. We address the multi-satellite scheduling problem with limited observation capacities that arises from the need to observe a set of targets on the Earth’s surface using imaging resources installed on a set of satellites. We define and analyze the conflict indicators of all available visible time windows of missions, as well as the feasible time intervals of resources. The problem is then formulated as a mixed integer linear programming model, in which constraints are derived from a careful analysis of the interdependency between feasible time intervals that are eligible for observations. We apply the proposed model to several different problem instances that reflect real-world situations. The computational results verify that our approach is effective for obtaining optimum solutions or solutions with a very good quality.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2018.11.058