Application of Quantum Annealing to Supply Chain Planning under Uncertainty

Supply chain planning is a complex optimization problem that involves the coordination of multiple resources in order to reduce costs and maximize benefit. In this study, we propose a QUBO formulation for the scheduling of the transportation of materials to a factory with uncertain arrival times. The standard methods like network flow or MILP solvers escalate exponentially with the problem parameters, making computation times grow rapidly. Quantum annealing is a candidate for an optimization poccess that can solve this computationally intense problems, and thus it is important to be able to find compatible formulations. The model proposed makes use of the QUBO formulation to give optimal solutions to the scheduling with unceratinty. We will establish the problem from a stochastic optimization point of view, and then build a hamiltonian that encodes it, following the QUBO formulation. With the quick increase in qubit numbers of quantum computers, this model seems like a promising tool that will provide solutions to very relevant and computionally hard problems in the near future.