  We present a subproblemation scheme for heuristical solving of the JSP (Job
Reassignment Problem). The cost function of the JSP is described via a QUBO
hamiltonian to allow implementation in both gate-based and annealing quantum
computers. For a job pool of $K$ jobs, $\mathcal{O}(K^2)$ binary variables --
qubits -- are needed to solve the full problem, for a runtime of
$\mathcal{O}(2^{K^2})$. With the presented heuristics, the average variable
number of each of the $D$ subproblems to solve is $\mathcal{O}(K^2/2D)$, and
the expected total runtime $\mathcal{O}(D2^{K^2/2D})$, achieving an exponential
speedup.
