dev-front/pack-route-tasks

pack_route tasks

100 tasks for the pack_route RL environment (Verifiers v1). In each task an agent is the dispatcher at a parcel station. It decides which parcels ride in which van and the order each van visits its stops, to minimise total driving time. The stops, parcels and travel times come from real 2018 Amazon Last Mile routes. The van fleet is synthetic.

Packing the vans first and routing second is a trap: it loses to the joint pack+route reference on every task. So an agent only reaches 1.0 by planning packing and routing together.

Config	Split	Tasks	Stops / vans	Reference
v1 (default)	train	80: 20 each from Los Angeles, Seattle, Chicago, Boston	20/3, 25/3, 30/4, 40/4, mixed	best found
v1	test	10 from Austin, a city train never sees	same mix	best found; plans removed
v0	test	10 (the first release)	14/3	proven optimum

from datasets import load_dataset
train = load_dataset("dev-front/pack-route-tasks", "v1", split="train")

How a task is made

1. Take one real route: one van's day from one station (2018-07-20 to 2018-08-24 in v1).
2. Pick 20–40 of its stops, spread across the route, with their real parcels (23–155 per task) and the real driving times between them and the station.
3. Add a synthetic fleet of 3 or 4 vans, sized so the parcels fill 85–95% of it.
4. Solve it with CP-SAT, jointly packing and routing. At this size a laptop rarely proves the optimum, so v1 uses the best plan found: the search stops after 30 s without improvement (at most 180 s), and the cheapest of its plan and the baselines becomes the reference. The solver's lower bound is stored too.
5. Keep the task only if the best "pack first, then route" plan grades ≤ 0.9 against the reference.

Every task comes from a different route. Every reference plan was checked with the environment's validator at exactly the solver's cost.

Fields

Field	Meaning
id	<route>-spread-n<stops>-k<vans>-t<fill>-s<seed>
instance.stops	Stop ids S01… The depot is row/column 0 of travel_s, and stops follow in this order
instance.parcels	id, stop, volume_dl (volume in decilitres, rounded from real dimensions)
instance.vans, instance.capacity_dl	Fleet size and per-van capacity. Both are synthetic
instance.travel_s	Real historical driving times in seconds; asymmetric
reference.optimum_s	The reference total driving time the grade is measured against
reference.optimality	proven (v0: the proven optimum) or best_found (v1: the best plan known)
reference.lower_bound_s	No plan is cheaper (CP-SAT's bound; median 13% below optimum_s in v1)
reference.floor_s	Depot legs no plan can avoid; used to normalise the gap
reference.ffd_s, reference.stop_aware_best_s, reference.stop_aware_mean_s	Pack-then-route baseline costs
reference.plans	The reference, FFD and best stop-aware plans in the environment's plan format; null in v1 test
meta (v1)	Route id, city, station, date, stops, vans, fill, build seconds, and the stop-aware baseline's grade

reference is the answer key. The environment never shows it to the agent; keep it out of prompts. The v1 test split keeps the costs needed for grading but not the plans.

Grade (PortSim reward v3)

Plan	Score
Valid	0.2 + 0.8·e^(−g/0.5), with g = max(0, cost − optimum_s) / (optimum_s − floor_s + 100)
Invalid	0.2 × share of cleanly delivered parcels
None submitted	0

Matching or beating the reference scores 1.0. In v1 a model can beat it, since it is not proven optimal.

Baselines on v1: the best stop-aware plan (keep each stop in one van where possible, then route each van optimally) scores 0.29–0.79 (mean 0.58, 11% more driving). First-fit-decreasing scores 0.25–0.70. On v0 FFD scores 0.32–0.67.

Not modelled yet

Time windows, service time, driver shifts and mid-episode news.

Licence and attribution

Derived from the 2021 Amazon Last Mile Routing Research Challenge dataset, CC BY-NC 4.0, and released under the same licence (non-commercial). Cite: Merchán et al., "2021 Amazon Last Mile Routing Research Challenge: Data Set", Transportation Science (2022), doi:10.1287/trsc.2022.1173. The reward formula and the "real records + solver optimum" pattern come from PortSimEnv (FineEnvs).