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In this paper, we address the problem of reconfiguring Earth observationsatellite constellation systems through multiple stages. The Multi-stageConstellation Reconfiguration Problem (MCRP) aims to maximize the totalobservation rewards obtained by covering a set of targets of interest throughthe active manipulation of the orbits and relative phasing of constituentsatellites. In this paper, we consider deterministic problem settings in whichthe targets of interest are known a priori. We propose a novel integer linearprogramming formulation for MCRP, capable of obtaining provably optimalsolutions. To overcome computational intractability due to the combinatorialexplosion in solving large-scale instances, we introduce two computationallyefficient sequential decision-making methods based on the principles of amyopic policy and a rolling horizon procedure. The computational experimentsdemonstrate that the devised sequential decision-making approaches yieldhigh-quality solutions with improved computational efficiency over the baselineMCRP. Finally, a case study using Hurricane Harvey data showcases theadvantages of multi-stage constellation reconfiguration over single-stage andno-reconfiguration scenarios.

Deterministic Multi-stage Constellation Reconfiguration Using Integer Linear Programing and Sequential Decision-Making Methods