  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.
