LLM-Guided Heuristic Design from Simulation Traces: A Case Study in Dynamic Production and AGV Scheduling

2026-08-10

AI Summary

This work proposes a novel paradigm that integrates discrete-event simulation with a large language model (Gemini-1.5-Pro) to overcome the limitations of traditional simulation-based optimization, which treats simulators as black boxes and offers little insight into policy failure. By leveraging event-level trajectory replay, the method automatically identifies bottlenecks from low-scoring simulation runs and generates interpretable, traceable, code-level policy revisions in parallel. It pioneers the use of simulation trajectories to guide the LLM in targeted heuristic rule modification, combined with rolling evaluation and an elite retention mechanism for iterative policy improvement. Evaluated on dynamic production and AGV scheduling tasks, the approach achieves an average policy score of 77.51 (out of 100), improving the best run from 62.49 to 78.61, and significantly outperforms MILP, handcrafted rules, and metaheuristic baselines across 100 random seeds and fault perturbations.

Abstract

Simulation-based optimization (SBO) evaluates executable policies under stochastic dynamics, but most methods treat the simulator as a black box: aggregate scores rank candidates without revealing why they fail or which policy logic should change. We present an LLM-guided heuristic design framework that uses repeated simulation for selection and event-level traces for diagnosis. Each incumbent is assessed through multiple replications, while replaying its lowest-scoring one produces a queryable trace. A manager agent formulates bottleneck hypotheses from this evidence, and editing agents implement parallel code-level revisions. After execution checks and repeated evaluation, best-so-far selection retains only improvements. LLM revision occurs between evaluation batches, while a fixed policy controls each simulation run. We evaluate the framework in a discrete-event simulation of dynamic production and automated guided vehicle (AGV) scheduling. Across five independent optimization runs with Gemini-3.1-Pro, final mean scores averaged 77.51 on the simulator's 0-100 scale. In the highest-scoring run, trace-based diagnoses motivated proactive charging, distance-aware AGV assignment, and rebalanced dispatch priorities, raising the best-so-far mean score from 62.49 to 78.61. On 100 matched seeds, the best final policy outscored representative rolling-MILP, rule-based, and metaheuristic policies on every seed and retained its advantage under random faults without re-optimization. After separate re-optimization for a longer horizon and variable order interarrival times, the resulting policies again outscored all baselines. Ablations with two LLM backbones showed that removing either parallel candidate generation or trace-database access reduced final mean scores. These results show that simulation traces can guide targeted code-level policy improvement in complex simulation-based scheduling.

Problem

• simulation-based optimization
• policy improvement
• black-box simulation
• heuristic design
• trace analysis

Innovation

• LLM-guided optimization
• simulation traces
• heuristic design
• AGV scheduling
• code-level policy revision