Hierarchical Framework for Space Exploration Campaign Schedule Optimization
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Gollins, Nicholas
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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.
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Date
2024
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