Retrieval-Augmented LLM-Guided Multi-Objective Ant Colony Optimization for Multi-AGV Path Planning in a U-Shaped Automated Container Terminal

Coordinating automated guided vehicles (AGVs) on shared roads is essential to efficient container-terminal transport. This study aims to improve routing and parameter adaptation in a U-shaped automated container terminal. We develop an improved ant colony optimization (IACO) algorithm combining direction-guided search, time-window conflict checks, and waiting or local replanning. A retrieval-augmented large language model recommends bounded parameter updates from domain literature and simulation experience, while IACO constructs and evaluates routes. In simulations on a 227-node network, IACO reduced mean makespan by 25.5–56.9% relative to conventional ant colony optimization, particle swarm optimization, and Rf-Ant, with shorter routes and fewer conflict-avoidance stops. A fleet-size sensitivity analysis examined 200–1200 transport tasks and 24–40 AGVs. At 100 tasks and 24 AGVs, ten tuning rounds across three scenarios selected parameters evaluated in 50 held-out scenarios; the 95% confidence interval for composite-score improvement was above zero. Retrieval yielded additional gains in most second-round task–fleet settings. Among four LLM backends, qwen3-max achieved the highest average improvement (3.60%) and beneficial-adjustment rate (73.6%). The approach supports conflict-aware routing and workload-sensitive fleet and parameter decisions in automated terminals.