Travel time optimization on multi-AGV routing by reverse annealing

TL;DR

This paper presents a quantum annealing-based approach for optimizing multi-AGV routing to minimize travel time, demonstrating significant speedups over classical solvers and traditional methods.

Contribution

It introduces a novel formulation for multi-AGV routing control and applies reverse annealing to enhance solution quality and speed, showcasing the potential of quantum annealing in logistics.

Findings

Reverse annealing improves solution quality over standard quantum annealing.

The method achieves up to 10 times faster performance than Gurobi.

Simulation confirms faster distribution compared to greedy algorithms.

Abstract

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.