  In this paper, we show the design and implementation of a quantum algorithm
for industrial shift scheduling (QISS), which uses Grover's adaptive search to
tackle a common and important class of valuable, real-world combinatorial
optimization problems. We give an explicit circuit construction of the Grover's
oracle, incorporating the multiple constraints present in the problem, and
detail the corresponding logical-level resource requirements. Further, we
simulate the application of QISS to specific small-scale problem instances to
corroborate the performance of the algorithm, and we provide an open-source
repository with our code, available on github.com/anneriet/QISS . Our work
shows how complex real-world industrial optimization problems can be formulated
in the context of Grover's algorithm, and paves the way towards important tasks
such as physical-level resource estimation for this category of use cases.
