Optimization Model and Solution Algorithm for Space Station Cargo Supply Planning under Complex Constraints

To enhance the efficient utilization of space resources, it is critical to integrate information from various systems of the space station and formulate scientific and effective methods for planning cargo supplies. Considering the large-scale, multi-objective, complex nonlinear, non-convex, non-differentiable, and mixed-integer characteristics, this study decomposes the space station cargo supply planning problem into a bi-level optimization problem involving cargo manifest and loading layout iterations. A new CILPSO algorithm is proposed to solve this by integrating particle coding, reliability priority, and random generation mechanisms of population initialization, global and local versions of particle updating, and a local search strategy. The experimental results show that the CILPSO algorithm outperforms other algorithms regarding search performance and convergence efficiency. The proposed approach can effectively reduce the cargo supply cost of the space station and improve the output of space science and application achievements. It provides a decision-making basis for the responsible department to develop cargo supply schemes, for the cargo supply systems to submit cargo demands, and for the cargo spaceship system to design loading schemes. This study advances the logistics sustainability of the space station.