This paper develops a hierarchical reinforcement learning architecture for multimission spaceflight campaign design under uncertainty, including vehicle design, infrastructure deployment planning, ...

This paper develops a flexibility management framework for space logistics mission planning under uncertainty through decision rules and multistage stochastic programming. It aims to add built-in f...

The trend of space commercialization is changing the decision-making process for future space exploration architectures, and there is a growing need for a new decision-making framework that explici...

This paper addresses the problem of reconfiguring Earth observation satellite constellation systems through multiple stages. The Multistage Constellation Reconfiguration Problem (MCRP) aims to maximize the total observation rewards obtained by covering a set of targets of interest through the active manipulation of the orbits and relative phasing of constituent satellites. This paper considers deterministic problem settings in which the targets of interest are known a priori. We propose a novel integer linear programming formulation for MCRP, capable of obtaining provably optimal solutions. To overcome computational intractability due to the combinatorial explosion in solving large-scale instances, we introduce two computationally efficient sequential decision-making methods based on the principles of a myopic policy and a rolling horizon procedure. The computational experiments demonstrate that the devised sequential decision-making approaches yield high-quality solutions with improved computational efficiency over the baseline MCRP. Finally, a case study using Hurricane Harvey data showcases the advantages of multistage constellation reconfiguration over single-stage and no-reconfiguration scenarios.

As humanity prepares for crewed missions to Mars, these will be among the most extended and isolated journeys, presenting challenges in team performance and dynamics. The extreme isolation, prolonged confinement, and necessity for autonomous decision-making without Earth support demand a comprehensive understanding of crew interactions and psychological resilience. This study addresses this need by integrating psychological theories with agent-based modeling (ABM) to simulate the impact of team composition over a 500-day Mars mission. Utilizing the Five-Factor Model (FFM) of personality traits, agents varying in openness, conscientiousness, neuroticism, extraversion, and agreeableness were modeled to form both heterogeneous and homogeneous teams. To capture functional as well as psychological diversity, the framework also incorporates variation in skills and roles, enabling a 2 × 2 factorial design that disentangles the effects of personality heterogeneity, role heterogeneity, and their interaction. Our results suggest that team composition influences stress, health, performance, and cohesion, with personality variation and role specialization contributing distinctly. When combined, these factors appear to generate interaction effects relevant for crew selection, training, and preparation. These findings underscore the importance of jointly considering personality and functional diversity when developing strategies to support resilience and effectiveness in long-duration spaceflight.