  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.
