Self-adaptive production scheduling for discrete manufacturing workshop using multi-agent cyber physical system

At present, the production control process of a discrete manufacturing workshop is characterized by high concurrency, mixed production lines and difficulty in prediction, which lead to uncertainty caused by dynamic disturbances and challenges in production control. Traditional system architectures struggle to handle these uncertainties flexibly and adaptively. To address these issues, an adaptive production scheduling system for the workshop is proposed, utilizing the Multi-agent Cyber Physical System (CPS-MAS) framework. This system integrates self-organization mechanisms and self-adaptive decision-making mechanisms to achieve cooperative optimal control of manufacturing resources. Using multi-agent technology, the resource model in the information space is encapsulated into an intelligent Cyber Physical System (CPS)-Agent model with cognitive interaction and autonomous decision-making capabilities. The improved contract network protocol (CNP) is utilized to the constructed agent, enabling their collaboration and competition to support the self-organization, negotiation, and assignment of manufacturing tasks. Based on multi-agent real-time perception and interactive negotiation, an adaptive control model of the manufacturing process is constructed based on Proportion Integration Differentiation (PID) control principle. This model is trained with the multi-layer perceptron that integrates an attention mechanism. The production strategy and parameters of the agent cooperative network are dynamically adjusted to enable dynamic decision-making optimization under disturbances. The proposed method is verified by experiments in scenarios involving machine failure, emergency order insertion and due date changes, proving its effectiveness.