Framework for Modeling and Optimization of On-Orbit Servicing Operations under Demand Uncertainties

TL;DR

This paper presents a comprehensive framework combining space logistics modeling and rolling horizon optimization to improve on-orbit servicing operations under demand uncertainties, demonstrated through case studies.

Contribution

It extends existing space logistics methods by integrating mixed-integer linear programming with rolling horizon decision-making for on-orbit servicing.

Findings

Effective short-term operational scheduling demonstrated.

Long-term strategic planning under demand uncertainty validated.

Framework adapts to diverse market conditions.

Abstract

This paper develops a framework that models and optimizes the operations of complex on-orbit servicing infrastructures involving one or more servicers and orbital depots to provide multiple types of services to a fleet of geostationary satellites. The proposed method extends the state-of-the-art space logistics technique by addressing the unique challenges in on-orbit servicing applications, and integrate it with the Rolling Horizon decision making approach. The space logistics technique enables modeling of the on-orbit servicing logistical operations as a Mixed-Integer Linear Program whose optimal solutions can efficiently be found. The Rolling Horizon approach enables the assessment of the long-term value of an on-orbit servicing infrastructure by accounting for the uncertain service needs that arise over time among the geostationary satellites. Two case studies successfully demonstrate the effectiveness of the framework for (1) short-term operational scheduling and (2) long-term strategic decision making for on-orbit servicing architectures under diverse market conditions.