mpee 0.4.1

MPEE — offline route calculations and optimization. A self-contained Rust routing + VRP engine you drive from Python or the `mpee` CLI: point-to-point routes, N×N tables, and multi-vehicle delivery optimization, all offline from a downloaded map.

MPEE — Offline route calculations and optimization

pip install mpee

A self-contained routing + delivery-optimization engine you drive from Python or a command line. Download a map once, then compute everything offline — no API keys, no per-request billing, no data leaving your machine:

• 🧭 Point-to-point routes — driving distance + time between two coordinates.
• 🚚 Multi-vehicle optimization (VRP) — best routes for N vehicles over your own stops, with capacities.
• 🗺️ Bring your own map — any OpenStreetMap extract (a city, a country).
• ⚡ Fast & small — a Rust engine (CH routing + brooom VRP solver) using CPU and GPU, with a footprint under ~50 MB.

The engine is built from two Rust crates — dijeng (contraction-hierarchy routing) and brooom (the VRP solver). Together they solve a 50,000-customer VRP on a laptop without ever materialising a full distance matrix, and in head-to-head tests on a Mac they produced shorter routes than VROOM at equal runtime, using less memory.

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Install

pip install mpee

Wheels are prebuilt for Linux / macOS / Windows (Python 3.8+). No Rust toolchain needed to install.

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1. Get a map (once)

# Download an OpenStreetMap extract from Geofabrik …
mpee download europe/great-britain/england/greater-london

# … and preprocess it into a routable cache (.pp + .ch).
# Seconds for a city, a few minutes for a whole country.
mpee build data/greater-london-latest.osm.pbf          # car (default)
# mpee build data/greater-london-latest.osm.pbf bicycle  # or a profile (car|bicycle|foot)

After this you are fully offline. The cache is reusable forever — a re-run of mpee build reuses it instantly (pass --force to rebuild, --quiet to hush progress).

2. Route from A to B

mpee route 51.5080,-0.1281 51.5138,-0.0984 --cache data/greater-london-latest.osm.pbf

distance: 2.38 km
duration: 4.4 min
from (snapped): (51.50753, -0.12802)
to   (snapped): (51.51328, -0.09844)

3. Optimize a delivery run over many stops

# stops.txt: one "lat,lon" per line (or a JSON [[lat,lon], …])
mpee optimize --stops stops.txt --vehicles 5 --capacity 20 \
    --cache data/greater-london-latest.osm.pbf

stops: 50  vehicles used: 3/5  unassigned: 0
total: 60.0 km, 115 min  (solved in 4.6s)
  vehicle 1: 14 stops, 17.2 km, 33 min
  vehicle 2: 16 stops, 19.3 km, 37 min
  vehicle 4: 20 stops, 23.5 km, 45 min

Tune the fleet with --vehicles and --capacity; bound the search with --time SECONDS; pin the depot with --depot LAT,LON (defaults to the centroid of the stops).

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Use it from Python

ℹ️ Coordinate order. The simple helpers take (lat, lon): route(lat, lon, …), optimize([(lat, lon), …]), snap(lat, lon), table([(lat, lon), …]). The VROOM-style solve(problem) accepts a coordinate in any of three forms — to avoid the ordering question entirely, use {"lat": …, "lon": …}:

"location": {"lat": 51.5138, "lon": -0.0984}   # explicit (recommended)
"location": [-0.0984, 51.5138]                  # or VROOM [lon, lat]
"location": {"coord": [-0.0984, 51.5138]}       # or the struct form

import mpee

# The .pp/.ch cache comes from a one-time `mpee download` + `mpee build`
# (see "Get a map" above) — or build it straight from Python:
#   mpee.Router.build("data/greater-london-latest.osm.pbf")   # → .pp + .ch
r = mpee.Router("data/greater-london-latest.osm.pbf.pp",
                "data/greater-london-latest.osm.pbf.ch")

# Distance + time between two points (set geometry=True for the path).
leg = r.route(51.5080, -0.1281, 51.5138, -0.0984)
print(f"{leg['distance_km']:.2f} km, {leg['duration_min']:.1f} min")

# Optimize 50 deliveries across 5 vehicles.
stops = [(51.51, -0.12), (51.49, -0.10), ...]   # your (lat, lon) list
plan = r.optimize(stops, vehicles=5, capacity=20, time_limit_s=5.0)
print(plan["total_distance_km"], "km over", plan["vehicles_used"], "vehicles")
for route in plan["routes"]:
    for stop in route["stops"]:
        print(route["vehicle_id"], stop["order"], stop["lat"], stop["lon"])

# Other helpers:
r.snap(51.50, -0.12)          # nearest routable road node
r.table(stops)                # full N×N duration + distance table
r.bbox()                      # coverage of the loaded map

Router.build("map.osm.pbf", profile="car") builds a cache from Python too (profile is car | bicycle | foot). It reuses an existing cache and returns instantly ({"cached": True}) unless you pass force=True, and it prints build progress by default — pass progress=False to silence it when driving the library from code:

info = mpee.Router.build("data/region-latest.osm.pbf", progress=False)
# {'pp_path': ..., 'ch_path': ..., 'nodes': ..., 'build_secs': ..., 'cached': False}

(The mpee build CLI mirrors this: --quiet silences progress, --force rebuilds an existing cache.)

Real fleets: per-vehicle & per-stop constraints

For mixed fleets and constrained jobs, use Router.solve(problem) with a VROOM-style problem (JSON). It exposes the engine's full model — vehicle types, capacities, skills, time windows and distinct start/end depots:

import json, mpee
r = mpee.Router("data/greater-london-latest.osm.pbf.pp", "data/greater-london-latest.osm.pbf.ch")

problem = {
  "vehicles": [
    # A big van and a faster motorcycle from the same depot ([lon, lat]):
    {"id": 1, "start": {"coord": [-0.1278, 51.5074]}, "end": {"coord": [-0.1278, 51.5074]},
     "capacity": [200], "speed_factor": 1.0},
    {"id": 2, "start": {"coord": [-0.1278, 51.5074]}, "end": {"coord": [-0.0984, 51.5138]},  # ends elsewhere
     "capacity": [40], "speed_factor": 1.6, "skills": [7]},          # only this vehicle has skill 7
  ],
  "jobs": [
    {"id": 101, "location": {"coord": [-0.0837, 51.4954]}, "delivery": [5], "skills": [7]},  # → must use the skilled vehicle
    {"id": 102, "location": {"coord": [-0.1196, 51.5098]}, "delivery": [30],                 # a heavy package (weight 30)
     "time_windows": [{"start": 0, "end": 3600}]},                                           # deliver within the first hour
    {"id": 103, "location": {"coord": [-0.0890, 51.5161]}, "delivery": [12], "priority": 100},# keep this one if capacity is tight
    # ... up to tens of thousands of stops ...
  ],
}

plan = r.solve(json.dumps(problem), time_limit_s=5.0)
for route in plan["routes"]:
    print("vehicle", route["vehicle_id"], "→",
          [s["job_id"] for s in route["stops"]], f"{route['distance_km']:.1f} km")
print("unassigned:", plan["unassigned"])   # over-capacity / outside every window / unreachable

Per vehicle	Per stop
capacity (multi-dimensional)	delivery / pickup (multi-dim package sizes / weights)
skills — which jobs it may serve	skills — required vehicle capability
speed_factor — e.g. a motorcycle at 1.6	time_windows — allowed arrival times
time_window — the driver's shift	service — time spent at the stop
max_travel_time / max_distance	priority — which jobs to keep when demand > capacity
distinct start / end locations

solve() serves every job it feasibly can and returns the rest in unassigned (over capacity, outside all time windows, or no road to them) — it never invents an impossible route.

Plan a work week (multi-day, multiple depots)

Model a week by giving each driver one vehicle per day, each bound to that day's shift window; pin a delivery to a weekday with a matching time window.

DAY = lambda k: {"start": k*86400 + 8*3600, "end": k*86400 + 18*3600}   # 08:00–18:00 on day k (Mon=0 … Fri=4)
depots = [[-0.1278, 51.5074], [-0.1300, 51.5230]]                       # two start/end points ([lon, lat])

vehicles = []
for depot in depots:
    for _driver in range(5):       # 5 drivers per depot → 10 drivers
        for k in range(5):         # Mon–Fri → one vehicle per driver per day
            vehicles.append({"id": len(vehicles) + 1,
                             "start": {"coord": depot}, "end": {"coord": depot},
                             "capacity": [1000], "time_window": DAY(k)})

# Each order is scheduled on a weekday by giving it that day's time window.
jobs = [{"id": o["id"], "location": {"coord": o["coord"]}, "delivery": [o["weight"]],
         "time_windows": [DAY(o["day"])]} for o in my_orders]

plan = r.solve(json.dumps({"vehicles": vehicles, "jobs": jobs}), time_limit_s=5.0)

Every driver starts and ends at their own depot, deliveries land on their scheduled day, and each route stays inside the 8-hour shift. Drop a job's time_windows to let the optimizer place it on whatever day is cheapest.

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Build from source (development)

pip install mpee needs no toolchain. To build the extension from a checkout, activate a venv first — maturin develop fails without an active one:

cd crates/mpee-py
python3 -m venv venv && source venv/bin/activate    # ← activate BEFORE maturin
pip install maturin
maturin develop --release                            # builds + installs `mpee`

Cross-platform wheels are built in CI with maturin (see the repo). examples/flask_app.py shows serving the solver over a Flask HTTP API (pip install flask && python3 examples/flask_app.py).

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How it works

pip install mpee ships a compiled Rust extension (mpee._mpee) plus a thin Python CLI. All routing and optimization run in-process — the map cache is memory-mapped, so opening it is near-instant and peak RAM stays low. Nothing is sent to a server.

MPEE stands for Morten Punnerud-Engelstad Engine. MIT licensed. Source: github.com/punnerud/mpee.