This paper proposes a way to model uncertainties and to introduce them explicitly in the design process of a preliminary space mission. Traditionally, a system margin approach is used in order to take them into account. In this paper, Evidence Theory is proposed to crystallise the inherent uncertainties. The design process is then formulated as an Optimisation Under Uncertainties (OUU). Three techniques are proposed to solve the OUU problem: (a) an evolutionary multi-objective approach, (b) a step technique consisting of maximising the belief for different levels of performance, and (c) a clustering method that firstly identifies feasible regions. The three methods are applied to the BepiColombo mission and their effectiveness at solving the OUU problem are compared.

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This paper proposes a way to model uncertainties and to introduce them explicitly in the design process of a preliminary space mission. Traditionally, a system margin approach is used in order to take them into account. In this paper, instead, evidence theory is proposed to crystallize the inherent uncertainties. The design process is then formulated as an optimisation under uncertainty (OUU) problem. An evolutionary multi-objective approach is used to solve the OUU. Two formulations of the OUU are analyzed: a bi-objective formulation and a complete belief function optimisation. The BepiColombo mission is used as a test case to investigate the benefits of the proposed method and to compare the two formulations