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Space mission planning and spacecraft design are tightly coupled and need tobe considered together for optimal performance; however, this integratedoptimization problem results in a large-scale Mixed-Integer NonlinearProgramming (MINLP) problem, which is challenging to solve. In response to thischallenge, this paper proposes a new solution approach to this MINLP problem byiterative solving a set of coupled subproblems via the augmented Lagrangiancoordination approach following the philosophy of Multi-disciplinary DesignOptimization (MDO). The proposed approach leverages the unique structure of theproblem that enables its decomposition into a set of coupled subproblems ofdifferent types: a Mixed-Integer Quadratic Programming (MIQP) subproblem formission planning and one or more Nonlinear Programming (NLP) subproblem(s) forspacecraft design. Since specialized MIQP or NLP solvers can be applied to eachsubproblem, the proposed approach can efficiently solve the otherwiseintractable integrated MINLP problem. An automatic and effective method to findan initial solution for this iterative approach is also proposed so that theoptimization can be performed without the need for a user-defined initialguess. In the demonstration case study, a human lunar exploration missionsequence is optimized with a subsystem-level parametric spacecraft designmodel. Compared to the state-of-the-art method, the proposed formulation canobtain a better solution with a shorter computational time even withoutparallelization. For larger problems, the proposed solution approach can alsobe easily parallelizable and thus is expected to be further advantageous andscalable.

Multidisciplinary Design Optimization Approach to Integrated Space Mission Planning and Spacecraft Design