DVRPTW-Bench: A Benchmark for Dynamic Vehicle Routing

Dynamic vehicle routing with time windows (DVRPTW) requires planning routes as requests are revealed during operation. Solvers need to coordinate planning timing, dispatch, and routing over diverse, evolving request snapshots while maintaining computational efficiency. Existing benchmarks typically either use a small number of solvers to study problem difficulty or compare solver performance under fixed demand settings, therefore overlooking differences in the advantage of various methods across different scenarios. DVRPTW-Bench addresses this gap by systematically evaluating 17 approaches across 135 demand configurations with 10 instances each. The evaluated approaches cover three planning components: decision timing, dispatch, and routing. And demand is modeled through four factors: problem scale, degree of dynamism (DoD), temporal arrival patterns, and spatial demand evolution. Experiments show higher DoD increases learned dispatch's advantage in distance per served customer (DPC) relative to rolling hybrid genetic search (HGS). Temporal concentration reduces DPC more for HGS than for neural constructors, while dispersed arrivals give learned dispatch a larger advantage. Strong bursts can also reverse the benefits of urgency triggers for some neural constructors on large instances. Spatial hotspot shifts yield larger DPC reductions for HGS than for neural constructors on large instances. The benchmark suite and code are available at https://anonymous.4open.science/r/iclr-dvrptw0925.