A Synergistic Multi-Agent Framework for Resilient and Traceable Operational Scheduling from Unstructured Knowledge

In capital-intensive industries, operational knowledge is often trapped in unstructured technical manuals, creating a barrier to efficient and reliable maintenance planning. This work addresses the need for an integrated system that can automate knowledge extraction and generate optimized, resilient, operational plans. A synergistic multi-agent framework is introduced that transforms unstructured documents into a structured knowledge base using a self-validating pipeline. This validated knowledge feeds a scheduling engine that combines multi-objective optimization with discrete-event simulation to generate robust, capacity-aware plans. The framework was validated on a complex maritime case study. The system successfully constructed a high-fidelity knowledge base from unstructured manuals and the scheduling engine produced a viable, capacity-aware operational plan for 118 interventions. The optimized plan respected all daily (6) and weekly (28) task limits, executing 64 tasks on their nominal date, bringing 8 forward, and deferring 46 by an average of only 2.0 days (95th percentile 4.8 days) to smooth the workload and avoid bottlenecks. An interactive user interface with a chatbot and planning calendar provides verifiable “plan-to-page” traceability, demonstrating a novel, end-to-end synthesis of document intelligence, agentic AI, and simulation to unlock strategic value from legacy documentation in high-stakes environments.