Global optimization of space mission concept of operations and systems design via mixed-integer nonlinear programming
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Abstract
Simultaneously optimizing space mission Concepts of Operations (ConOps) and spacecraft systems design involves discrete choices and nonconvex systems design models. This paper formulates the problem as a mixed-integer nonlinear program (MINLP) and presents both a heuristic approach based on augmented Lagrangian decomposition and a global optimization scheme that exploits concave structures commonly found in space systems. Specifically, we consider a crewed Mars mission case study developed in collaboration with NASA’s Advanced Concepts Office. To efficiently find feasible solutions to this MINLP, a decomposition framework from our prior work is modified and applied, where a series of convex mixed-integer quadratic programs and nonconvex nonlinear programs are iteratively solved. To identify globally optimal solutions, the proposed scheme incorporates the decomposition method as an upper bounding heuristic, constructs tight relaxations via piecewise linear underestimators, and applies variable domain reduction to close the optimality gap. The case study results demonstrate high computational efficiency and robust convergence behavior for both approaches. In particular, the proposed global optimization scheme converges to the same solution found by a state-of-the-art MINLP solver with substantially reduced computational cost, often by a few orders of magnitude. These results indicate the potential of the proposed framework as a practical and scalable alternative for solving nonconvex MINLPs in space mission and ConOps optimization coupled with systems design.
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2026
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Creative Commons Attribution 4.0 International (CC BY 4.0)