Space exploration plans are becoming increasingly complex as public agencies and private companies target deep-space locations, such as cislunar space and beyond, which require long-duration missions and many supporting systems and payloads. Optimizing multimission exploration campaigns is challenging due to the large number of required launches as well as their sequencing and compatibility requirements, making conventional space logistics formulations unscalable. To tackle this challenge, this paper proposes an alternative approach that leverages a two-level hierarchical optimization algorithm: an evolutionary algorithm is used to explore the campaign scheduling solution space, and each of the solutions is then evaluated using a time-expanded multicommodity flow mixed-integer linear program. A number of case studies, focusing on the Artemis lunar exploration program, demonstrate how the method can be used to analyze potential campaign architectures. The method enables a potential mission planner to study the sensitivity of a campaign to program-level parameters such as logistics vehicle availability and performance, payload launch windows, and in situ resource utilization infrastructure efficiency.

随着公共机构和私营公司瞄准深空位置，例如地月空间及更远的地方，太空探索计划变得越来越复杂，这需要长期任务和许多支持系统和有效载荷。由于所需的发射次数及其排序和兼容性要求，优化多任务探索活动具有挑战性，使得传统的太空后勤方案无法扩展。为了应对这一挑战，本文提出了一种利用两级分层优化算法的替代方法：使用进化算法来探索活动调度解决方案空间，然后使用时间扩展的多商品流混合来评估每个解决方案-整数线性规划。许多以阿耳忒弥斯月球探测计划为重点的案例研究展示了如何使用该方法来分析潜在的活动架构。该方法使潜在的任务规划者能够研究战役对程序级参数的敏感性，例如后勤车辆的可用性和性能、有效载荷发射窗口以及现场资源利用基础设施效率。