Scheduling Optimization and Adaptive Decision‑Making Method for Self‑organizing Manufacturing Systems Considering Dynamic Disturbances

The production mode of manufacturing industry presents characteristics of multiple varieties， small-batch and personalization， leading to frequent disturbances in workshop. Traditional centralized scheduling methods are difficult to achieve efficient and real-time production management under dynamic disturbance. In order to improve the intelligence and adaptability of production scheduler， a novel distributed scheduling architecture is proposed， which has the ability to autonomously allocate tasks and handle disturbances. All production tasks are scheduled through autonomous collaboration and decision-making between intelligent machines. Firstly， the multi-agent technology is applied to build a self-organizing manufacturing system， enabling each machine to be equipped with the ability of active information interaction and joint-action execution. Secondly， various self-organizing collaboration strategies are designed to effectively facilitate cooperation and competition among multiple agents， thereby flexibly achieving global perception of environmental state. To ensure the adaptability and superiority of production decisions in dynamic environment， deep reinforcement learning is applied to build a smart production scheduler. Based on the perceived environment state， the scheduler intelligently generates the optimal production strategy to guide the task allocation and resource configuration. The feasibility and effectiveness of the proposed method are verified through three experimental scenarios using a discrete manufacturing workshop as the test bed. Compared to heuristic dispatching rules， the proposed method achieves an average performance improvement of 34.0% in three scenarios in terms of order tardiness. The proposed system can provide a new reference for the design of smart manufacturing systems.