Abstract: Aiming at the problem of unclear system performance and low optimization in Robotic Mobile Fulfillment System (RMFS) with multiple Automatic Guided Vehicle (AGV) distributed scheduling,a centralized scheduling approach based on digital twin was proposed.A digital twin model with physical system,virtual system,twin data center and support services for system operation was established.Instruction acceptance and data acquisition mechanism for physical system,scheme generation and simulation mechanism for virtual system,and data processing and comparison mechanism for twin data center were established.System support service was used to ensure the smooth operation of the three mechanisms.Through the data interaction between physical system and virtual system,the real-time scheduling was ensured when orders continue to arrive.According to the different disturbances in RMFS,the rescheduling rules were determined to generate new schemes.The accurately predict the state of the system in the future,collectively optimize system performance,and effectively deal with disturbances were verified by instances of centralized scheduling approach based on digital twin compared with traditional methods.

Key words: robotic mobile fulfillment system, digital twin, centralized scheduling, real-time scheduling, rescheduling rules, automatic guided vehicle

摘要： 针对具有多辆自动导引小车的多机器人存取系统在分布式调度时系统性能不明确、优化程度低的难题,提出一种基于数字孪生的集中式调度方法。建立了以物理系统、虚拟系统、孪生数据中心和系统支撑服务为核心的数字孪生模型,提出物理系统接收指令和采集数据机制、虚拟系统方案生成与仿真机制、孪生数据中心数据处理和对比机制,并基于系统支撑服务确保这3种机制互相映射。通过物理系统和虚拟系统的数据交互预测未来的情况,实现动态拣货单情形下的实时调度。针对系统中发生的各种扰动,提出新的重调度规则进行调度优化。通过实例验证,与传统方法相比,基于数字孪生的集中式调度能够准确预测未来一段时间的系统状态,并能整体优化系统性能,有效处理扰动。

关键词: 多机器人存取系统, 数字孪生, 集中式调度, 实时调度, 重调度规则, 自动导引小车