论文标题
通过反向退火对多AGV路由的旅行时间优化
Travel time optimization on multi-AGV routing by reverse annealing
论文作者
论文摘要
自D-Wave Systems于2011年生产第一台商业机器以来，量子退火已被积极研究。控制大型自动导向车辆的车队是利用量子退火的真实世界应用之一。在这项研究中，我们提出了一种配方，以控制旅行路线，以最大程度地减少旅行时间。我们通过在虚拟工厂中的仿真来验证我们的配方，并与不考虑总体绕道距离的贪婪算法相比，验证了更快的分布的有效性。此外，我们利用反向退火来最大程度地利用D-Wave的量子退火器的优势。从快速贪婪算法获得的相对较好的解决方案开始，反向退火搜索周围的更好的解决方案。与仅标准量子退火相比，我们的反向退火方法可提高性能，并且比强大的经典求解器Gurobi快10倍。这项研究扩展了在多AGV系统的应用中使用一般问题解决器的优化的使用，并揭示了反向退火作为优化器的潜力。
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.
