Δ-MILP: Deep Space Network Scheduling via Mixed-Integer Linear Programming

• Published in: IEEE access Vol. 10; pp. 41330 - 41340
• Main Authors: Claudet, Thomas, Alimo, Ryan, Goh, Edwin, Johnston, Mark D., Madani, Ramtin, Wilson, Brian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Antennas; Deep Space Network; Deep-space communications; Integer programming; Job shop scheduling; Linear programming; Mixed integer; NASA; Optimal scheduling; Optimization; optimization methods; Schedules; Scheduling; Space vehicles
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3164213

This paper introduces <inline-formula> <tex-math notation="LaTeX">\Delta </tex-math></inline-formula>-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, <inline-formula> <tex-math notation="LaTeX">\Delta </tex-math></inline-formula>-MILP incorporates new sets of constraints including 1) splitting larger tracks into shorter segments and 2) preventing overlapping between tracks on different antennas. Additionally, <inline-formula> <tex-math notation="LaTeX">\Delta </tex-math></inline-formula>-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 <inline-formula> <tex-math notation="LaTeX">\Delta </tex-math></inline-formula>-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.