Deep Space Network Scheduling via Mixed Integer Linear Programming

NASA’s Deep Space Network (DSN) is a globally-spanning communications network responsible for supporting the interplanetary spacecraft missions of NASA and other international users. The DSN is a highly utilized asset, and the large demand for its’ services makes the assignment of DSN resources a daunting computational problem. In this paper, we cast the this challenging scheduling task as a Mixed-Integer Linear Programming (MILP) optimization. We offer a novel randomization technique which is capable solving the resulting MILP formulation for large large-scale instances of the DSN scheduling. A real-world DSN dataset is utilized to demonstrate the effectiveness of the proposed MILP model and randomization approach.