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As we strive to establish a long-term presence in space, it is crucial to plan large-scale space missions and campaigns with future uncertainties in mind. However, integrated space mission planning, which simultaneously considers mission planning and spacecraft design, faces significant challenges when dealing with uncertainties; this problem is formulated as a stochastic mixed integer nonlinear program (MINLP), and solving it using the conventional method would be computationally prohibitive for realistic applications. Extending a deterministic decomposition method from our previous work, we propose a novel and computationally efficient approach for integrated space mission planning under uncertainty. The proposed method effectively combines the Alternating Direction Method of Multipliers (ADMM)-based decomposition framework from our previous work, robust optimization, and two-stage stochastic programming (TSSP).This hybrid approach first solves the integrated problem deterministically, assuming the worst scenario, to precompute the robust spacecraft design. Subsequently, the two-stage stochastic program is solved for mission planning, effectively transforming the problem into a more manageable mixed-integer linear program (MILP). This approach significantly reduces computational costs compared to the exact method, but may potentially miss solutions that the exact method might find. We examine this balance through a case study of staged infrastructure deployment on the lunar surface under future demand uncertainty. When comparing the proposed method with a fully coupled benchmark, the results indicate that our approach can achieve nearly identical objective values (no worse than 1% in solved problems) while drastically reducing computational costs. 

Space mission planning and spacecraft design are tightly coupled and need to be considered together for optimal performance; however, this integrated optimization problem results in a large-scale Mixed-Integer Nonlinear Programming (MINLP) problem, which is challenging to solve. In response to this challenge, this paper proposes a new solution approach to this MINLP problem by iterative solving a set of coupled subproblems via the augmented Lagrangian coordination approach following the philosophy of Multi-disciplinary Design Optimization (MDO). The proposed approach leverages the unique structure of the problem that enables its decomposition into a set of coupled subproblems of different types: a Mixed-Integer Quadratic Programming (MIQP) subproblem for mission planning and one or more Nonlinear Programming (NLP) subproblem(s) for spacecraft design. Since specialized MIQP or NLP solvers can be applied to each subproblem, the proposed approach can efficiently solve the otherwise intractable integrated MINLP problem. An automatic and effective method to find an initial solution for this iterative approach is also proposed so that the optimization can be performed without the need for a user-defined initial guess. In the demonstration case study, a human lunar exploration mission sequence is optimized with a subsystem-level parametric spacecraft design model. Compared to the state-of-the-art method, the proposed formulation can obtain a better solution with a shorter computational time even without parallelization. For larger problems, the proposed solution approach can also be easily parallelizable and thus is expected to be further advantageous and scalable.