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Quantum annealing has been actively researched since D-Wave Systems producedthe first commercial machine in 2011. Controlling a large fleet of automatedguided vehicles is one of the real-world applications utilizing quantumannealing. In this study, we propose a formulation to control the travelingroutes to minimize the travel time. We validate our formulation throughsimulation in a virtual plant and authenticate the effectiveness for fasterdistribution compared to a greedy algorithm that does not consider the overalldetour distance. Furthermore, we utilize reverse annealing to maximize theadvantage of the D-Wave's quantum annealer. Starting from relatively goodsolutions obtained by a fast greedy algorithm, reverse annealing searches forbetter solutions around them. Our reverse annealing method improves theperformance compared to standard quantum annealing alone and performs up to 10times faster than the strong classical solver, Gurobi. This study extends a useof optimization with general problem solvers in the application of multi-AGVsystems and reveals the potential of reverse annealing as an optimizer.

Travel time optimization on multi-AGV routing by reverse annealing