Hierarchical Optimization via LLM-Guided Objective Evolution for Mobility-on-Demand Systems

Yi Zhang, Yushen Long, Yun Ni, Liping Huang, Xiaohong Wang, Jun Liu

Keywords: Dynamic Hierarchical Optimization, LLM-Optimizer Interaction, Mobility-on-Demand Systems, Meta-Objective Design, Feasibility-Aware Adaptation

TL;DR: We propose a training-free hybrid framework where LLMs generate high-level goals via prompt-based harmony search and optimizers enforce constraints in dynamic ride-hailing, outperforming RL, manual decomposition, and LLM-only baselines.

Abstract: Online ride-hailing platforms aim to deliver efficient mobility-on-demand services, often facing challenges in balancing dynamic and spatially heterogeneous supply and demand. Existing methods typically fall into two categories: reinforcement learning (RL) approaches, which suffer from data inefficiency, oversimplified modeling of real-world dynamics, and difficulty enforcing operational constraints; or decomposed online optimization methods, which rely on manually designed high-level objectives that lack awareness of low-level routing dynamics. To address this issue, we propose a novel hybrid framework that integrates large language model (LLM) with mathematical optimization in a dynamic hierarchical system: (1) it is training-free, removing the need for large-scale interaction data as in RL, and (2) it leverages LLM to bridge cognitive limitations caused by problem decomposition by adaptively generating high-level objectives. Within this framework, LLM serves as a meta-optimizer, producing semantic heuristics that guide a low-level optimizer responsible for constraint enforcement and real-time decision execution. These heuristics are refined through a closed-loop evolutionary process, driven by harmony search, which iteratively adapts the LLM prompts based on feasibility and performance feedback from the optimization layer. Extensive experiments based on scenarios derived from both the New York and Chicago taxi datasets demonstrate the effectiveness of our approach, achieving an average improvement of 16% compared to state-of-the-art baselines.

Primary Area: Optimization (e.g., convex and non-convex, stochastic, robust)

Submission Number: 16951