Robust mission planning based on nested Genetic Algorithm

Military mission planning involves resource allocation and task scheduling and it is a complex combination optimization problem. Traditionally, this problem is solved to get a static solution (plan) with minimal makespan. However, there are lots of uncertainties in the military operations, such as disruptions of actions or unexpected increases of task duration, a static plan is prone to be invalid. This paper focus on generating robust plan which can absorb some deviations. Time-slack based robustness measure is used to qualify the robustness, based on which a nested Genetic Algorithm is proposed to resolve the robust mission planning problem. At last, an illustrative mission instance is presented, three plans with different robustness are tested through simulations and the results provide strong evidence of the benefits of this method.