Han, PengGuo, YanningWang, PengyuLi, ChuanjiangPedrycz, Witold2024-05-192024-05-1920230018-92511557-9603https://doi.org10.1109/TAES.2023.3247552https://hdl.handle.net/20.500.12713/5444This article proposes a two-stage optimization framework to simultaneously solve the orbit design and mission scheduling problems of on-orbit refueling (OOR) systems in Sun-synchronous orbit (SSO). The fuel can be delivered to the client satellite (CS) by small-scale service satellites from the on-orbit fuel station (FS), which leads to a new FS-servicer-CS OOR mode. For the first stage, a modified spectral clustering-nonlinear programming (NLP) method is used to assign CSs to FSs, and design the orbit of FSs under J(2) perturbation. For the second stage, the mission scheduling problem is formulated as a mixed-integer NLP model and solved via the genetic quantum algorithm. Different from the existing literature, this article introduces a clustering distance metric combining multiple orbit characteristics for CS assignment, which can indirectly reduce the estimated fuel costs in the FS orbit design. Furthermore, the objective of reducing the number of servicers is first considered in the second stage. The effectiveness and superiority of the proposed two-stage framework are verified through several numerical simulations and comparison studies.eninfo:eu-repo/semantics/closedAccessOrbitsFuelsSatellitesPlanningCostsPerturbation MethodsOptimizationGenetic Quantum Algorithm (Gqa)Mission SchedulingOrbit DesignSpectral ClusteringSun-Synchronous Orbit (Sso)Article59549684983WOS:0011017913000142-s2.0-85151538163N/A10.1109/TAES.2023.3247552Q1