Human-Machine Collaborative Decision-Making Method under Emergency Scenario for Unmanned Warehouse

In modern operational environments, all work plans are susceptible to the influence of emergencies, such as equipment failures and resource shortages, underscoring the criticality of emergency management. Current solutions primarily rely on experienced human or employ machine scheduling models. Human-machine collaboration can leverage their respective strengths, thereby enhancing decision-making efficiency and safety. Based on this, a framework for human-machine collaborative emergency management is proposed in this paper. The emergency management process is divided into two stages: task selection and task scheduling. In the task selection phase, the Human-Machine Collaborative Decision-Making Algorithm for Dynamic Tasks (HMC-DMADT) is introduced to identify key nodes and generate task lists, and humans can correct and confirm lists they do not endorse. In the task scheduling phase, the Dynamic Task Scheduling Algorithm with Human Experience-Based Constraints (HEC-ETSA) is proposed, which integrates the Action-Mask mechanism with the Double Deep Q-Network (DDQN) algorithm to optimize action selection, ensuring decision safety and feasibility. Finally, a simulation platform is established to conduct numerous experiments within an unmanned warehousing scenario. The results demonstrate that the proposed human-machine collaborative emergency management framework effectively addresses emergencies in unmanned warehousing operations.