Reconfigurable Earth Observation Satellite Scheduling Problem

TL;DR

This paper introduces a novel reconfigurable Earth Observation Satellite Scheduling Problem (REOSSP), optimizing satellite constellations through maneuverability and a mixed-integer linear programming approach, with a rolling horizon method for large-scale problems.

Contribution

It develops a new formulation for reconfigurable satellite scheduling and proposes a solution method to improve efficiency and effectiveness over traditional fixed-configuration approaches.

Findings

Reconfigurable satellite constellations improve observation scheduling performance.

The mixed-integer linear programming formulation effectively models the REOSSP.

The rolling horizon procedure reduces computational time for large instances.

Abstract

Earth observation satellites (EOSs) play a pivotal role in capturing and analyzing planetary phenomena, ranging from natural disasters to societal development. The EOS scheduling problem (EOSSP), which optimizes the schedule of EOSs, is often solved with respect to nadir-directional EOS systems, thus restricting the observation time of targets and, consequently, the effectiveness of each EOS. This paper leverages state-of-the-art constellation reconfigurability to develop the reconfigurable EOS scheduling problem (REOSSP), wherein EOSs are assumed to be maneuverable, forming a more optimal constellation configuration at multiple opportunities during a schedule. This paper develops a novel mixed-integer linear programming formulation for the REOSSP to optimally solve the scheduling problem for given parameters. Additionally, since the REOSSP can be computationally expensive for large-scale problems, a rolling horizon procedure (RHP) solution method is developed. The performance of the REOSSP is benchmarked against the EOSSP, which serves as a baseline, through a set of random instances where problem characteristics are varied and a case study in which Hurricane Sandy is used to demonstrate realistic performance. These experiments demonstrate the value of constellation reconfigurability in its application to the EOSSP, yielding solutions that improve performance, while the RHP enhances computational runtime for large-scale REOSSP instances.