机器人移动物流系统中实时的存储分配的监督学习和树搜索
Supervised learning and tree search for real-time storage allocation in Robotic Mobile Fulfillment Systems
摘要：
机器人移动物流系统是一种自动化的零件到拣选系统，特别适合电子商务仓储。这种类型的仓库的一个显著特点是其高度的存储模块化。多个机器人同时移动货架，在拣选作业完成后，货架可以返回到任何开放位置。这项工作的重点是实时的【形】存储分配问题，以尽量减少机器人的旅行时间。一种高效但计算成本高的蒙特卡罗树搜索方法被离线使用，以生成高质量的体验。这种经验可以通过具有适当的基于坐标的特征表示的神经网络来学习。所获得的神经网络被用作几个新的存储策略中的动作预测器，无论是按原样还是在推出和监督树搜索策略中。所得到的性能水平取决于在决策步骤中可用的计算时间，并且与文献中的实时的【形】决策规则相比始终更好。
英文摘要：
A Robotic Mobile Fulfillment System is a robotised parts-to-picker system that is particularly well-suited for e-commerce warehousing. One distinguishing feature of this type of warehouse is its high storage modularity. Numerous robots are moving shelves simultaneously, and the shelves can be returned to any open location after the picking operation is completed. This work focuses on the real-time storage allocation problem to minimise the travel time of the robots. An efficient -- but computationally costly -- Monte Carlo Tree Search method is used offline to generate high-quality experience. This experience can be learned by a neural network with a proper coordinates-based features representation. The obtained neural network is used as an action predictor in several new storage policies, either as-is or in rollout and supervised tree search strategies. Resulting performance levels depend on the computing time available at a decision step and are consistently better compared to real-time decision rules from the literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
