From Real Roads to Depot-Scale Routing: A Dataset and Benchmark for Electric Vehicle Routing with Time Windows

Solvers for the Electric Vehicle Routing Problem with Time Windows (EVRP-TW) are commonly evaluated on small synthetic benchmarks. Whether conclusions drawn from such benchmarks generalize to operational workloads on real road networks remains unclear. We introduce EVRP-TW-D/B, a public dataset-and-benchmark suite. EVRP-TW-D provides 71,500 instance views across 11 U.S. cities and 100–2,000 customers. Its two-stage generator builds reusable city environments from directed road networks and instantiates delivery days using joint demand, service-time, and time-window tuples from Amazon records. EVRP-TW-B compares five learning-based policies with exact and metaheuristic baselines using a common task and objective, released implementations, independent verification, and declared budgets. With 100-customer Euclidean training, only two policies achieve lower mean costs than the best evaluated search baseline under a 30-minute budget at 500 and 1,000 customers. Training the same architectures on 100-customer EVRP-TW-D instances yields full feasibility and lower mean costs than this baseline at both scales. Policies trained at 1,000 customers retain this advantage beyond the training geography. These findings motivate evaluating solver competitiveness across operational workloads and geographies, with EVRP-TW-D/B providing a reproducible foundation.