I am working on a crew scheduling problem formulated as a MIP binary optimization where each employee is represented by a binary variable $X_{ids}$ s.t. $i \in I$ is $i$th employee, $d \in D$ is the day number and $s \in S$ is the shift (ex: 9AM-12PM) and if the employee is scheduled to work on that day at that shift the variable is 1 otherwise 0.
The set $I = \bigcup J_i$ subsets such that $J_i$ partition the set $I$ and where each $J_i$ represents a subset of priority employees. For example if $i \in [1,2,3]$ then $J_1$ takes precedence over $J_2$ and $J_2$ over $J_3$ in terms of shift scheduling. I want to enforce this condition via the constraints in the optimization instead of the objective function coefficients, but am unsure how to do so. The sets $J_i$ that partition the set $I$ are pre-defined. I'd like to keep this as a linear optimization due to the size of the original problem.