Route Optimization Dataset for Large-Scale AGV Systems with 1000 units in Japanese Logistics Warehouses

This study proposes a new approach for optimizing the routing of 1,000 Automated Guided Vehicles (AGVs) in large-scale logistics warehouses using Quantum Annealing (QA). As logistics operations grow, efficient AGV route planning is essential to ensure smooth, safe, and time-efficient transportation, especially in high-density AGV environments. By utilizing quantum mechanics, QA effectively addresses the complex challenges inherent in large-scale AGV management. 本研究提出了一种面向大型物流仓库内1000台自动导引车（Automated Guided Vehicles）的路径优化新方法，该方法基于量子退火（Quantum Annealing，QA）技术。随着物流业务规模不断扩张，高效的AGV路径规划对于保障运输过程顺畅、安全且时效可控至关重要，在高密度AGV作业环境中这一点尤为关键。借助量子力学原理，量子退火能够有效应对大规模AGV管理中固有的复杂挑战。

搜集汇总

背景与挑战

背景概述

该数据集专注于日本物流仓库中1000台自动导引车（AGV）的大规模路径优化问题，利用量子退火（QA）技术来应对高密度AGV环境下的复杂管理挑战。其核心目标是提升运输效率、安全性和顺畅性，适用于大规模物流操作场景。