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November 18, 2025Journal of Spacecraft and Rockets0 citations

Request-Centric Modeling and Architecture Optimization for Earth–Moon Space Logistics

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RARiccardo ApaJHJennifer HudsonMRMarcello Romano

Key Points

  • Optimization framework enhances servicing operations for multiple client satellites, focusing on efficiency.
  • Multi-objective optimization identifies tradeoffs in fuel consumption and task completion time for servicing.
  • Methodology employs a two-level heuristic optimization algorithm for trajectory and depot location decisions.
  • Results highlight improvement potential in servicing operations for spacecraft in low Earth orbit.

Abstract

This work presents a campaign-level optimization framework for designing space transportation architectures to support servicing operations in cislunar orbit. The goal is to service multiple client satellites—requiring refueling, inspection, and repair—using a fleet of high- and low-thrust spacecraft. A multi-objective optimization approach determines the optimal number and type of servicing spacecraft, their trajectories, and orbital depot locations, aiming to maximize the number of fulfilled servicing requests while minimizing both fuel consumption and task completion time. The cislunar region is represented by six orbital nodes: low Earth orbit, geostationary transfer orbit, geostationary Earth orbit, Earth–moon L1 and L2 halo orbits, and low lunar orbit. To manage the computational complexity, a two-step approach is adopted. First, transfer trajectory costs between orbital nodes are computed for both high-thrust and low-thrust servicing spacecraft using four transfer strategies: i) bi-impulsive minimum-Formula: see text direct transfers, ii) flyby-supported minimum-Formula: see text transfers, iii) minimum-time direct transfers, and iv) invariant manifold transfers. Second, these costs are integrated into a two-level heuristic optimization algorithm with embedded feasibility constraints. The resulting Pareto-optimal front highlights tradeoffs among feasibility, affordability, and effectiveness of multiple potential solutions. The methodology is tested on two one-year servicing mission scenarios involving three client satellite constellations.

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Cite This Study

Apa et al. (2025) studied this question.

synapsesocial.com/papers/6924fef4c0ce034ddc351d72https://doi.org/10.2514/1.a36495
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Low-Thrust Propulsion and Drift Orbit Optimization for Multi-Client Servicing Missions in Low Earth Orbit2026
  2. 2Minimum-Fuel On-Orbit Servicing via A Search Algorithm*2026
  3. 3On-Orbit Servicing Networks in Cislunar Space: A Framework for Orbit Selection, Transfer Design, and Scheduling2025
  4. 4On-Orbit Servicing Networks in Cislunar Space: A Framework for Orbit Selection, Transfer Design, and Scheduling2025 · 2 citations
  5. 5Hybrid Optimization Technique for Finding Efficient Earth–Moon Transfer Trajectories2026