On-Orbit Servicing-Integrated Maintenance Strategy for Satellite Constellation

TL;DR

This paper develops an integrated maintenance strategy for satellite constellations using on-orbit servicing, optimizing costs and service levels through a bi-objective model and case study validation.

Contribution

It introduces a bi-objective optimization framework for satellite maintenance combining on-orbit servicing and inventory management, with real-world case validation.

Findings

OOS reduces maintenance costs by up to 14.5%.

OOS decreases launch and manufacturing costs by approximately 25%.

Maintenance strategies are mostly invariant, while pricing varies along the Pareto frontier.

Abstract

This paper proposes a maintenance strategy for a satellite constellation that utilizes on-orbit servicing (OOS). Under this strategy, the constellation operator addresses satellite failures in two ways: by deploying new satellites and by recovering failed satellites through OOS. We develop an inventory management model with a parametric replenishment policy for the maintenance process, which can evaluate the performance of the satellite constellation system. Based on this model, we formulate the interaction between the constellation operator -- who seeks to maintain the required service level of the constellation while minimizing maintenance cost -- and the OOS provider -- who seeks to maximize profit by selecting service price and performance levels -- as a bi-objective optimization problem and identify the corresponding Pareto-optimal solutions. A case study based on real-world-scale constellation and launch service shows that, relative to the benchmark strategy without OOS, the OOS-integrated solutions can reduce annual maintenance cost by up to 14.5%, while reducing annual launch and manufacturing costs by approximately 25% each and maintaining the required service levels. The results further show that, within a given scenario, the Pareto-optimal set is generally generated by an almost invariant maintenance strategy on the constellation operator side, whereas most variation along the Pareto frontier is driven by pricing decisions of the OOS provider side. Among the OOS-related parameters, the fraction of failures that can be recovered through OOS has the strongest structural effect.