论文标题
基于拼图的存储系统中多项目检索的强化学习
Reinforcement learning for multi-item retrieval in the puzzle-based storage system
论文作者
论文摘要
如今，快速交付服务已经创造了对高密度仓库的需求。基于拼图的存储系统是提高存储密度的实用方法，但是，在检索过程中面临困难。在这项工作中，开发了一种深厚的增强学习算法，特别是Double＆Duuling Deep Q Network，旨在解决系统中具有一般设置的多项目检索问题，其中多个所需的项目，伴游和I/O点被随机放置。此外，我们提出了一个一般紧凑的整数编程模型来评估解决方案质量。广泛的数值实验表明，增强学习方法可以产生高质量的解决方案，并且表现优于三种相关的最新启发式算法。此外，提出了一种转换算法和分解框架来分别处理同时运动和大规模实例，从而提高了PBS系统的适用性。
Nowadays, fast delivery services have created the need for high-density warehouses. The puzzle-based storage system is a practical way to enhance the storage density, however, facing difficulties in the retrieval process. In this work, a deep reinforcement learning algorithm, specifically the Double&Dueling Deep Q Network, is developed to solve the multi-item retrieval problem in the system with general settings, where multiple desired items, escorts, and I/O points are placed randomly. Additionally, we propose a general compact integer programming model to evaluate the solution quality. Extensive numerical experiments demonstrate that the reinforcement learning approach can yield high-quality solutions and outperforms three related state-of-the-art heuristic algorithms. Furthermore, a conversion algorithm and a decomposition framework are proposed to handle simultaneous movement and large-scale instances respectively, thus improving the applicability of the PBS system.
