Optimizing Resource-Constrained Logistics Operations for On-Orbit Space Science Programs

The space station is a critical platform for on-orbit scientific research and space-based experiments, capable of accommodating on the order of a thousand scientific experiments over the course of a decade. However, on the logistics operations front, managing such a substantial number of scientific experiments is a highly complex task. The need to consider the spacecraft launch schedule, diverse experimental fields, and resource constraints related to astronaut workload, ascent and descent spacecraft capacities, experimental facilities, and on-orbit sample storage renders this planning problem intractable for traditional planning methods. To address this research gap, we propose a mixed-integer linear programming (MILP) model for the integrated logistics planning of on-orbit scientific experiments under resource constraints. The MILP model aims to maximize the spatiotemporal capacity utilization of the space station facilities while ensuring equitable resource distribution across scientific subjects and satisfying all constraints. We apply the proposed model in a comprehensive case study using real-world data from the Tiangong space station. The results show that while maximizing the outcomes of on-orbit space science programs, the solution also provides insights into system bottlenecks and multiple design tradeoffs in the process.