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pip install rtv-solver
from rtv_solver import OnlineRTVSolver
# Initialize the RTV solver with the URL of the OSRM server
online_rtv_solver = OnlineRTVSolver("http://127.0.0.1:50000/")
payload = {
"requests": [
{
'am': int,
'wc': int,
'time_windows' : [
{'pickup_time_window_start': int, 'pickup_time_window_end': int, 'dropoff_time_window_start': int, 'dropoff_time_window_end': int,},
],
'pickup_pt': {'lat': float, 'lon': float, 'node_id': int},
'booking_id': int,
'dropoff_time_window_start': int,
'dropoff_time_window_end': int,
'dropoff_pt': {'lat': float, 'lon': float, 'node_id': int}
}],
"driver_runs": driver_runs
}
feasibility = online_rtv_solver.check_feasibility(payload)
feasibility <-- [(feasible_window,vmt/pmt ratio)]
current_time = 5*3600+30*60 # 05:30:00 pm
driver_runs, unserved_requests = online_rtv_solver.solve_pdptw_rtv(new_payload)
unserved_requests <-- [list of ids of the requests that are not feasible to serve]
driver_runs, unserved_requests = online_rtv_solver.solve_pdptw_heuristic(new_payload)
unserved_requests <-- [list of ids of the requests that are not feasible to serve]
new_payload = {
"depot": {},
"requests": [],
"driver_runs": []
}
driver_runs = online_rtv_solver.serve_asap(new_payload)
current_time = 5*3600+40*60+00 # Simulate to 05:40:00 pm
new_driver_runs = online_rtv_solver.simulate_manifest(current_time, driver_runs)
payload = {
"driver_runs": driver_runs,
"depot": depot
}
driver_runs = online_rtv_solver.resolve_pdptw_rtv(payload)
depot = {
"pt": {"lat": val, "lon": val}
}
driver_runs <- generated driver runs
feasibility, stats = online_rtv_solver.get_stats(depot, driver_runs)
feasibility {true or false} <- Is this schedule feasibile
stats = {
"vmt": ,
"pmt": ,
"serviced": ,
"wait_time": [wait time for each request],
"detour": [detour for each request]
}
{
'depot': {
'loc': {'lat': float, 'lon': float, 'node_id': int}
},
'date': 'yyyy-mm-dd',
'driver_runs': [],
'requests': []
}
{
'requests': [ {
'am': int,
'wc': int,
'pickup_time_window_start': int,
'pickup_time_window_end': int,
'pickup_pt': {'lat': float, 'lon': float, 'node_id': int},
'booking_id': int,
'dropoff_time_window_start': int,
'dropoff_time_window_end': int,
'dropoff_pt': {'lat': float, 'lon': float, 'node_id': int}
}]
}
{
'DriverRun': [ {
'state': {
'run_id': int,
'start_time': int,
'end_time': int,
'am_capacity': int,
'wc_capacity': int,
'locations_already_serviced': int,
'locations_dt_seconds': int,
'loc': {'lat': float, 'lon': float, 'node_id': int},
'total_locations': int,
},
'manifest': Stop[list]
}]
}
{
'Stop': [ {
'run': int,
'booking_id': int,
'order': int,
'action': string,
'loc': {'lat': float, 'lon': float, 'node_id': int}
'scheduled_time': int,
'am': int,
'wc': int,
'time_window_start': int,
'time_window_end': int,
}]
}
wget https://download.geofabrik.de/north-america/us/north-carolina-latest.osm.pbf
docker run -t -v "${PWD}:/data" ghcr.io/project-osrm/osrm-backend osrm-extract -p /opt/car.lua /data/north-carolina-latest.osm.pbf || echo "osrm-extract failed"
docker run -t -v "${PWD}:/data" ghcr.io/project-osrm/osrm-backend osrm-partition /data/north-carolina-latest.osrm || echo "osrm-partition failed"
docker run -t -v "${PWD}:/data" ghcr.io/project-osrm/osrm-backend osrm-customize /data/north-carolina-latest.osrm || echo "osrm-customize failed"
docker run -t -i -p 5000:5000 -v "${PWD}:/data" ghcr.io/project-osrm/osrm-backend osrm-routed --algorithm mld /data/north-carolina-latest.osrm
python -m build
twine upload dist/rtv_solver-[version]*
inputs/wilson
folder contains the
inputs/wilson
FAQs
A solver for real-time vehicle routing problems
We found that rtv-solver demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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