  In this paper, we address the problem of reconfiguring Earth observation
satellite constellation systems through multiple stages. The Multi-stage
Constellation Reconfiguration Problem (MCRP) aims to maximize the total
observation rewards obtained by covering a set of targets of interest through
the active manipulation of the orbits and relative phasing of constituent
satellites. In this paper, we consider deterministic problem settings in which
the targets of interest are known a priori. We propose a novel integer linear
programming formulation for MCRP, capable of obtaining provably optimal
solutions. To overcome computational intractability due to the combinatorial
explosion in solving large-scale instances, we introduce two computationally
efficient sequential decision-making methods based on the principles of a
myopic policy and a rolling horizon procedure. The computational experiments
demonstrate that the devised sequential decision-making approaches yield
high-quality solutions with improved computational efficiency over the baseline
MCRP. Finally, a case study using Hurricane Harvey data showcases the
advantages of multi-stage constellation reconfiguration over single-stage and
no-reconfiguration scenarios.
