Quantum annealing has been actively researched since D-Wave Systems produced the first commercial machine in 2011. Controlling a large fleet of automated guided vehicles is one of the real-world applications utilizing quantum annealing. In this study, we propose a formulation to control the traveling routes to minimize the travel time. We validate our formulation through simulation in a virtual plant and authenticate the effectiveness for faster distribution compared to a greedy algorithm that does not consider the overall detour distance. Furthermore, we utilize reverse annealing to maximize the advantage of the D-Wave’s quantum annealer. Starting from relatively good solutions obtained by a fast greedy algorithm, reverse annealing searches for better solutions around them. Our reverse annealing method improves the performance compared to standard quantum annealing alone and performs up to 10 times faster than a commercial classical solver, Gurobi. This study extends a use of optimization with general problem solvers in the application of multi-AGV systems and reveals the potential of reverse annealing as an optimizer.

Quantum annealing has been actively researched since D-Wave Systems produced the first commercial machine in 2011. Controlling a large fleet of automated guided vehicles is one of the real-world applications utilizing quantum annealing. In this study, we propose a formulation to control the traveling routes to minimize the travel time. We validate our formulation through simulation in a virtual plant and authenticate the effectiveness for faster distribution compared to a greedy algorithm that does not consider the overall detour distance. Furthermore, we utilize reverse annealing to maximize the advantage of the D-Wave's quantum annealer. Starting from relatively good solutions obtained by a fast greedy algorithm, reverse annealing searches for better solutions around them. Our reverse annealing method improves the performance compared to standard quantum annealing alone and performs up to 10 times faster than the strong classical solver, Gurobi. This study extends a use of optimization with general problem solvers in the application of multi-AGV systems and reveals the potential of reverse annealing as an optimizer.

<div class="section abstract"><div class="htmlview paragraph">To accelerate development and improve the quality of car navigation systems, we have built a system for automatic generation of evaluation courses. In general, the operation of car navigation systems is verified by driving tests using vehicles. The evaluation courses need to be designed so that inspection sites, such as underground parking lots, tunnels, etc., will be visited during the evaluation period. They should be circuits that include as many inspection sites as possible within a defined distance. However, as the number of the inspection sites increases, the number of courses that can be designed becomes enormous. This makes it difficult to create courses that meet all of the requirements. Hence engineers have spent a lot of time on evaluation course design. For this reason, automatic course generation has become essential for reducing man-hours. We believe that one of the effective approaches is to treat automatic evaluation course generation as a combinatorial optimization problem. In our formulation, inspection sites are grouped into clusters according to the required number of courses, and the shortest circuit is constructed in each cluster.</div><div class="htmlview paragraph">Then, we treat the clustering and shortest circuit generation problems separately as a bi-level combinatorial optimization problem. In other words, the original problem is divided into smaller parts of the combinatorial optimization problems. We then propose a Markov chain Monte Carlo method for solving the bi-level optimization problem, and construct a system for automatic generation of evaluation courses. The proposed method significantly reduces course-design time compared to manual course construction.</div></div>