  We present a novel method for task optimization in industrial plants using
quantum-inspired tensor network technology. This method allows us to obtain the
best possible combination of tasks on a set of machines with a set of
constraints without having to evaluate all possible combinations. We simulate a
quantum system with all possible combinations, perform an imaginary time
evolution and a series of projections to satisfy the constraints. We improve
its scalability by means of a compression method, an iterative algorithm, and a
genetic algorithm, and show the results obtained on simulated cases.
