AMHSTrafficLab: An Industrial Benchmark for Routing and Regional Traffic Control in Large-Scale Resource-Competitive Transport Networks

In industrial settings, such as automated material handling systems (AMHS) in modern semiconductor manufacturing, transport networks must serve uneven, continuously arriving demand with large vehicle fleets and limited track space. Routing decisions interact with rules that control resource competition, including vehicle-following rules, right-of-way mechanisms, and regional admission control; together, they shape traffic flow and affect the risks of congestion, deadlock and throughput collapse. However, comparisons across industrial routing studies are difficult to reproduce when simulator implementations and accompanying control policies are incompletely documented. We introduce AMHSTrafficLab, an industrial benchmark for reproducibly evaluating and developing routing and regional traffic control in large-scale AMHS: it provides industrially grounded vehicle dynamics, track layouts, workloads, and standardized interfaces for classical, heuristic, and learning-based policies. Using the benchmark, we evaluate 13 routing methods across 7 fleet–workload scenarios and 3 regional-control settings. Our results show that without regional-control, the system deadlocks frequently regardless of the routing algorithm used, demonstrating that routing performance cannot be interpreted independently of the accompanying traffic-control policy. A proposed regional admission-control baseline reduces throughput-collapse runs from 265/910 to 9/910 and substantially suppresses local deadlocks. With regional control, we further compare different routing methods and find that learning-based methods do not consistently outperform congestion-aware heuristics, and their relative performance varies with workload and fleet density. These findings highlight the benchmark's value for evaluating routing and traffic control together and for developing future algorithms.