$\Delta$-MILP: Deep Space Network Scheduling via Mixed-Integer Linear Programming

TL;DR

$ ext{ extDelta}$-MILP is an advanced MILP-based framework that improves NASA's DSN scheduling by incorporating new constraints and heuristics, achieving perfect constraint satisfaction and fairness in complex, oversubscribed scenarios.

Contribution

The paper introduces $ ext{ extDelta}$-MILP, a novel extension of MILP for DSN scheduling that includes new constraints and heuristics for better feasibility and prioritization.

Findings

Achieves 100% constraint satisfaction in tested scenarios.

Provides fair scheduling among missions.

Outperforms previous methods in oversubscribed weeks.

Abstract

This paper introduces $\Delta$-MILP, a powerful variant of the mixed-integer linear programming (MILP) optimization framework to solve NASA's Deep Space Network (DSN) scheduling problem. This work is an extension of our original MILP framework (DOI:10.1109/ACCESS.2021.3064928) and inherits many of its constructions and strengths, including the base MILP formulation for DSN scheduling. To provide more feasible schedules with respect to the DSN requirements, $\Delta$-MILP incorporates new sets of constraints including 1) splitting larger tracks into shorter segments and 2) preventing overlapping between tracks on different antennas. Additionally, $\Delta$-MILP leverages a heuristic to balance mission satisfaction and allows to prioritize certain missions in special scenarios including emergencies and landings. Numerical validations demonstrate that $\Delta$-MILP now satisfies 100% of the requested constraints and provides fair schedules amongst missions with respect to the state-of-the-art for the most oversubscribed weeks of the years 2016 and 2018.