Route Optimization FAQ

The optimised routes all return with a 'geometry' object that is an encoded representation of the route polyline. They can convert that directly to a geojson object using our WebSDK utility function similar to this that we are doing in one of our notebooks.

    // we create a geojson object to send to our mapview elements
    // It will be a feature collection of all the route polylines
    checkStatus.result.routes.forEach((rte,idx) => {
    //console.log(rte);
    let feature = {
    type: 'Feature',
    properties: {
        id: rte.vehicle,
        vin: rte.description
      },
      geometry: {
        coordinates: nextbillion.utils.polyline.decode(rte.geometry,5)
       .map((c) => c.reverse()),
       type: 'LineString'
      }
    }
    
   

This demo has the possibility of generating a large number of MVRP transactions against the key being used https://observablehq.com/@nbai/example-vehicle-routing-problem

We don't have a limit on vehicles, A maximum of 2000 locations can be added to the locations object. Additionally, a maximum of 2000 tasks can be added in an optimization problem. Number of tasks is calculated as number of jobs + 2 * (number of shipments).

Traffic_timestamp specifies the time when the vehicle operates and we show the route based on the traffic data on considering the specified time and the value should be in unix seconds. This should be mentioned under Routing object

    "options": {
        "routing": {
        "traffic_timestamp": 1656302000
        }
    }
    

Our strategy is that we choose the route with least costs. If there are solutions that use a single vehicle at an equal cost compared to using multiple vehicles, we prefer using fewer vehicles. We suggest you use the "max_tasks" parameter in vehicles which will assign the jobs to the given vehicles. Refer to the example below,

  {
  "locations": {
    "id": 1,
    "description": "testing",
    "location": "1.29360227,103.80828989|1.29360227,103.8273062|1.30227739,103.84595327|1.31482858,103.84503015|1.31925839,103.86312334|1.3248957169,103.8285176106|1.2996042263,103.8112802999|1.2830643677,103.8343527086|1.2856021297,103.8384649827"
  },
  "vehicles": [
    {
      "id": 1,
      "start_index": 3,
      "end_index": 7,
      "capacity": [
        5
      ],
      "max_tasks": 1
    },
    {
      "id": 2,
      "start_index": 8,
      "end_index": 8,
      "capacity": [
        5
      ],
      "max_tasks": 2
    }
  ],
  "jobs": [
    {
      "id": 1,
      "location_index": 0,
      "service": 300
    },
    {
      "id": 2,
      "location_index": 1,
      "service": 300
    },
    {
      "id": 3,
      "location_index": 6,
      "service": 300
    }
  ]
}
    

We provide a dedicated parameter called the “skills” parameter for this use case. Based on the type of order received, for eg. If a customer places an order for ice cream and you need to assign this order to a vehicle with a cold storage facility, you can match the skill of the vehicle to that of the order.

It is used for representing a break that a driver can take during their route or during their shift timings.

    
    {
  "vehicles": [
    {
      "id": 1,
      "description": "14 Ft- Eicher/709-14 Ft",
      "capacity": [
        3500
      ],
      "skills": [
        1
      ],
      "max_tasks": 3,
      "fixed_cost": 1320,
      "time_window": [
        1689053400,
        1689186540
      ],
      "start_index": 0,
      "break": {
        "id": 1,
        "time_windows": [
          [
            1689071400,
            1689136200
          ]
        ],
        "service": 50400
      }
    }
  ]
}