City-LEO: Toward Transparent City Management Using LLM with End-to-End Optimization
<|reference_start|>City-LEO: Toward Transparent City Management Using LLM with End-to-End Optimization: Existing operations research (OR) models and tools play indispensable roles in smart-city operations, yet their practical implementation is limited by the complexity of modeling and deficiencies in optimization proficiency. To generate more relevant and accurate solutions to users' requirements, we propose a large language model (LLM)-based agent ("City-LEO") that enhances the efficiency and transparency of city management through conversational interactions. Specifically, to accommodate diverse users' requirements and enhance computational tractability, City-LEO leverages LLM's logical reasoning capabilities on prior knowledge to scope down large-scale optimization problems efficiently. In the human-like decision process, City-LEO also incorporates End-to-end (E2E) model to synergize the prediction and optimization. The E2E framework be conducive to coping with environmental uncertainties and involving more query-relevant features, and then facilitates transparent and interpretable decision-making process. In case study, we employ City-LEO in the operations management of e-bike sharing (EBS) system. The numerical results demonstrate that City-LEO has superior performance when benchmarks against the full-scale optimization problem. With less computational time, City-LEO generates more satisfactory and relevant solutions to the users' requirements, and achieves lower global suboptimality without significantly compromising accuracy. In a broader sense, our proposed agent offers promise to develop LLM-embedded OR tools for smart-city operations management.<|reference_end|>
@article{jiao2024city-leo:,
    title={City-LEO: Toward Transparent City Management Using LLM with End-to-End
  Optimization},
    author={Zihao Jiao, Mengyi Sha, Haoyu Zhang, Xinyu Jiang, Wei Qi},
    journal={arXiv preprint arXiv:2406.10958},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10958},
    primaryClass={math.OC cs.CL cs.MA}
}