A Node-Based Nash-Inspired D3QN RL Framework for Traffic–Energy Coordinated AGV Scheduling in Smart Warehouses
Year: 2026
Type: article
Abstract: Automated guided vehicle (AGV) scheduling in large warehouses becomes increasingly difficult as dense traffic and concentrated charging demands interact at shared intersections. Vehicle-centric multi-agent reinforcement learning (MARL) also faces increasing state dimensionality and non-stationarity as the fleet grows. This study therefore models controlled road-network nodes, rather than individual AGVs, as agents and develops a Nash-D3QN framework for traffic–energy coordinated scheduling. A* generates initial feasible paths, Nash-D3QN allocates intersection right-of-way using local and neighboring states, and a shared Petri-net layer enforces occupancy and safety constraints. State of charge (SOC), payload, start–stop losses, and charging queues are incorporated into the state, action, and reward designs to favor low-SOC and heavily loaded AGVs when necessary. In the 50-AGV scenario, Nash-D3QN shows numerically higher task completion rates than the compared baselines by 0.41–1.40 percentage points, together with 7.6–24.0% lower average waiting time, 4.7–46.0% higher SOC efficiency, and 8.38–32.42 percentage points higher coordination rates. Multi-seed analysis further evaluates task completion, coordination, SOC-based task efficiency, and waiting time at 50, 110, and 200 AGVs. With 200 AGVs, Nash-D3QN retains approximately 76.34% task completion, 2.68 tasks/SOC efficiency, and 86.17% coordination, indicating performance retention under larger tested system scales and operating loads.
Source: Applied Sciences
DOI: 10.3390/app162110725
Authors: Mohan Shu, Yusun Zhou, Bin Sun
Institutions: Zhejiang University, Hangzhou City University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7224313310