面向不确定性感知的人机协作订单拣选高效公平策略学习
Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative Human-Robot Order Picking
AI总结：
针对人机协作订单拣选的随机分配优化问题，提出基于图建模的多目标DRL方法，兼顾效率与公平性，性能优于基准且具备跨仓库规模的迁移能力。
摘要：
在人机协作订单拣选系统中，人类拣货员与自主移动机器人（Autonomous Mobile Robots, AMRs）在仓库内独立行进，在拣货点位会合，由拣货员将货物装载到AMRs上。本文研究了这类系统在随机环境下将拣货员分配给AMRs的优化问题。我们提出了一种新颖的多目标深度强化学习（Deep Reinforcement Learning, DRL）方法，用于学习有效的分配策略，在最大化拣选效率的同时，致力于提升人类拣货员之间的工作负载公平性。在该方法中，我们使用图对仓库状态进行建模，并定义了一种神经网络架构，该架构能够捕捉区域信息并有效提取与效率和工作负载相关的表征。我们开发了一个离散事件仿真模型，用于训练和评估所提出的DRL方法。实验表明，我们的方法能够找到非支配策略集，这些策略在公平性与效率目标之间实现了良好的权衡。经过训练的策略在效率和公平性两方面均优于基准方法。此外，在不同仓库规模的场景下测试时，这些策略表现出良好的迁移性。仿真模型、所提方法及实验的实现代码均已公开。
英文摘要：
In collaborative human-robot order picking systems, human pickers and Autonomous Mobile Robots (AMRs) travel independently through a warehouse and meet at pick locations where pickers load items onto the AMRs. In this paper, we consider an optimization problem in such systems where we allocate pickers to AMRs in a stochastic environment. We propose a novel multi-objective Deep Reinforcement Learning (DRL) approach to learn effective allocation policies to maximize pick efficiency while also aiming to improve workload fairness amongst human pickers. In our approach, we model the warehouse states using a graph, and define a neural network architecture that captures regional information and effectively extracts representations related to efficiency and workload. We develop a discrete-event simulation model, which we use to train and evaluate the proposed DRL approach. In the experiments, we demonstrate that our approach can find non-dominated policy sets that outline good trade-offs between fairness and efficiency objectives. The trained policies outperform the benchmarks in terms of both efficiency and fairness. Moreover, they show good transferability properties when tested on scenarios with different warehouse sizes. The implementation of the simulation model, proposed approach, and experiments are published.
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。
