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arXiv · 2111.11628

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

Thomas Claudet·Ryan Alimo·Edwin Goh·Mark Johnston·Ramtin Madani·Brian Wilson
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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.

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Thomas Claudet, Ryan Alimo, Edwin Goh, Mark Johnston, Ramtin Madani, Brian Wilson. 2021-11-23. $\Delta$-MILP: Deep Space Network Scheduling via Mixed-Integer Linear Programming. https://doi.org/10.1109/access.2022.3164213

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