Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol

2025-06-16

AI Summary

To address the limitations of traditional urban freight logistics optimization—namely, heavy reliance on manual coordination and poor scalability—this paper proposes a generative digital twin agent system built upon the Model Context Protocol (MCP). The system integrates generative AI, retrieval-augmented generation (RAG), structured memory, and a multimodal dialogue interface to autonomously invoke and coordinate the AnyLogic simulation engine and Gurobi optimization solver. It represents the first application of MCP in a digital twin architecture enabling cross-disciplinary tool interoperability. Unlike static visualization-based twins, our system supports dynamic planning, autonomous decision-making, and closed-loop execution. Evaluated in a low-carbon freight case study, it demonstrates strong modularity, interoperability, and adaptability, significantly improving optimization efficiency and decision intelligence. This work establishes a novel paradigm for intelligent transportation systems and sustainable urban governance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Optimizing urban freight logistics is critical for developing sustainable, low-carbon cities. Traditional methods often rely on manual coordination of simulation tools, optimization solvers, and expert-driven workflows, limiting their efficiency and scalability. This paper presents an agentic system architecture that leverages the model context protocol (MCP) to orchestrate multi-agent collaboration among scientific tools for autonomous, simulation-informed optimization in urban logistics. The system integrates generative AI agents with domain-specific engines - such as Gurobi for optimization and AnyLogic for agent-based simulation - forming a generative digital twin capable of reasoning, planning, and acting across multimodal freight networks. By incorporating integrated chatbots, retrieval-augmented generation, and structured memory, the framework enables agents to interpret user intent from natural language conversations, retrieve relevant datasets and models, coordinate solvers and simulators, and execute complex workflows. We demonstrate this approach through a freight decarbonization case study, showcasing how MCP enables modular, interoperable, and adaptive agent behavior across diverse toolchains. The results reveal that our system transforms digital twins from static visualizations into autonomous, decision-capable systems, advancing the frontiers of urban operations research. By enabling context-aware, generative agents to operate scientific tools automatically and collaboratively, this framework supports more intelligent, accessible, and dynamic decision-making in transportation planning and smart city management.

Problem

• Autonomous optimization of urban logistics using AI
• Integration of generative AI with scientific tools
• Enhancing decision-making in transportation and smart cities

Innovation

• Agentic system with model context protocol
• Generative digital twin for multimodal networks
• Integrated chatbots and retrieval-augmented generation