rtv-solver 0.1.8

A solver for real-time vehicle routing problems

Online-RTV

Installation

pip install rtv-solver

Code example

Initialize

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/")

Check feasibility of time slots

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)]

Generating a manifest

current_time = 5*3600+30*60 # 05:30:00 pm
driver_runs = online_rtv_solver.solve_rtv(current_time,new_payload)

Fast option

driver_runs = online_rtv_solver.solve_rtv_fast(new_payload)

Simulate the vehicles

current_time = 5*3600+40*60+00 # Simulate to 05:40:00 pm
new_driver_runs = online_rtv_solver.simulate_manifest(current_time,new_payload["date"],driver_runs)

Payload format

Common format

{
    
    'depot': {
        'loc': {'lat': float, 'lon': float, 'node_id': int}
    }, 
    'date': 'yyyy-mm-dd', 
    'driver_runs': [],
    'requests': []
    
}

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

{
    
    '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

{
    
    '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,
    }] 
    
}

rolling-horizen-RTV

Running

• Set up an osrm server. Follow https://github.com/Project-OSRM/osrm-backend
• cd into the src folder.
• run python main.py --server_url "" --input_file "" --out_put_dir "" --interval 300 --rh_factor 0 --max_cardinality 4
• Required parameters:
  • server_url: Url of the OSRM server (ex: "http://127.0.0.1:5000/")
  • input_file: path to the payload.pkl file
  • out_put_dir: directory to record outputs
  • interval: interval for the rolling horizon and batching
  • rh_factor: rolling horizon factor
  • max_cardinality: meximum size of shared trips

Set up the OSRM Server

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