An AI-Augmented BI Platform for Collaborative Production Logistics: A Multi-Agent Predictive Optimization Framework

Recent advances in logistics optimization have enabled AI-driven solutions for demand forecasting, route planning, and supply chain management, yet most existing systems rely on fixed feature engineering and centralized decision-making, limiting adaptability, scalability, and effective human–AI collaboration in dynamic environments. To address these challenges, we propose Adaptive Multi-Agent Logistics Optimization with Human–AI Coordination Learning, a three-stage framework that integrates context-aware feature generation, hybrid multi-agent decision-making with intelligent caching, and adaptive coordination learning. The proposed system dynamically adjusts feature representations to operational conditions, reduces decision latency through memory-based reasoning, and improves human–AI alignment via shared mental model adaptation. Experiments on COCO-Logistics, Solomon VRPTW, and real-world delivery datasets demonstrate substantial improvements over strong baselines in route accuracy, decision latency, fuel efficiency, and human acceptance rates, indicating that the proposed framework provides an effective and scalable solution for adaptive logistics optimization in complex operational settings.