Dynamic Scheduling Strategy for Complex Industrial Processes Based on Multi-Agent Collaboration

Abstract

The increasing complexity of modern industrial processes, characterized by frequent disturbances such as equipment failures and urgent order changes, demands more adaptive scheduling solutions. Traditional centralized scheduling methods often fail to address real time dynamics, while existing multi agent systems face challenges in balancing local autonomy with global optimization. This study proposes a novel dynamic scheduling strategy integrating multi agent collaboration with a credit based coordination mechanism to enhance responsiveness and efficiency in complex industrial environments. The research develops a three layer agent architecture comprising resource, task, and coordinator agents, linked through an event driven communication protocol. A hybrid negotiation framework enables both rapid response to emergencies and deliberative optimization for long term scheduling. The core innovation lies in a dynamic credit allocation model that evaluates agents' historical performance and collaborative contributions to guide task assignment. These findings advance distributed industrial control theory by formalizing the relationship between agent incentives and system wide performance. The proposed approach provides actionable insights for implementing Industry 4.0 adaptive scheduling in discrete manufacturing sectors.