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.

我们提出了一个大型语言模型（LLM）的代理人（“City-LEO”），通过交互式对话提高城市管理的效率和透明度，以生成更相关和准确的解决方案。在 E2E 框架的决策过程中，City-LEO 还整合了预测和优化，以应对环境不确定性，并实现透明和可解释的决策过程。在电动自行车共享系统的案例研究中，使用 City-LEO 的数值结果表明它在满面优化问题时具有卓越的性能，用较少的计算时间产生更满意和相关的解决方案，并且在不显著损害准确性的情况下实现更低的全局次优性。总体而言，我们提出的代理人为智慧城市运营管理开发了嵌入 LLM 的 OR 工具提供了希望。