Data Legal Compliance-Oriented Enterprise Production Scheduling Decision-Making Under a Multi-Agent Collaborative Framework

Abstract

As the new energy vehicle (NEV) industry is increasingly characterized by personalized customization and green manufacturing, conventional centralized production scheduling approaches have become less effective in coping with dynamic disturbances and the coordination requirements of distributed manufacturing resources. To address this issue, this study develops a five-layer collaborative scheduling decision framework consisting of the physical, perception, negotiation, learning, and optimization layers, thereby operationalizing a closed-loop logic of “perception–negotiation–learning–optimization.” Specifically, the perception layer acquires real-time production-state information through a digital twin environment. These findings demonstrate the functional completeness and operational feasibility of the proposed framework for dynamic scheduling in distributed manufacturing environments and provide both theoretical support and practical guidance for intelligent scheduling system design in smart factories.