Hierarchical Framework for Space Exploration Campaign Schedule Optimization

TL;DR

This paper introduces a hierarchical optimization framework combining genetic algorithms and mixed-integer linear programming to improve scheduling of complex multi-mission space exploration campaigns, demonstrated through Artemis lunar case studies.

Contribution

It presents a scalable two-level optimization method for space campaign scheduling, integrating genetic algorithms with flow-based MILP evaluations, addressing complexity issues in traditional models.

Findings

Effective analysis of Artemis campaign architectures

Sensitivity assessment of logistics parameters

Enhanced scheduling flexibility and robustness

Abstract

Space exploration plans are becoming increasingly complex as public agencies and private companies target deep-space locations, such as cislunar space and beyond, which require long-duration missions and many supporting systems and payloads. Optimizing multi-mission exploration campaigns is challenging due to the large number of required launches as well as their sequencing and compatibility requirements, making the conventional space logistics formulations not scalable. To tackle this challenge, this paper proposes an alternative approach that leverages a two-level hierarchical optimization algorithm: a genetic algorithm is used to explore the campaign scheduling solution space, and each of the solutions is then evaluated using a time-expanded multi-commodity flow mixed-integer linear program. A number of case studies, focusing on the Artemis lunar exploration program, demonstrate how the method can be used to analyze potential campaign architectures. The method enables a potential mission planner to study the sensitivity of a campaign to program-level parameters such as logistics vehicle availability and performance, payload launch windows, and in-situ resource utilization infrastructure efficiency.