Long Term Routing is a VRP for multiple days. When planning for multiple days, the optimisation gets even more complicated.

Input

An input for a solve request for a PVRP problem requires several data:

• period
• locations
• orders
• vehicles

Planning period

An extra property explaining the period for which we can solve the problem should be used.

{
  "period": {
      "start": "2018-01-01",
      "end": "2018-01-31"
  },
  // ...
}

Locations

Define the list of locations.

Orders

DateWindow

The date windows are a list of start/end and date-time combinations that describes the time availability of an order.
It has a starttime and endtime, a start date and an end date. You can indicate whether this availability is either hard, which means that it must be accounted for in the schedule. Optionally, you can define the weight on this availability constraint.

Fleet

The fleet consists of your vehicles, that you want to allocate the orders to. Each vehicle is defined by a starting and ending location (referencing a location on the network), and the shift start and end times.

The end location is optional and may be omitted. This may be useful in cases when you care less about total distance traveled, and instead would like to prioritize your visits closeby to be done as soon as possible. The reason why this works is because Solvice OnRoute minimizes total travel time, and may leave closeby visits for on the way back.

Optionally, you may define the capacity of the vehicle (using the same unit as the "weight" for your orders). The algorithm will make sure that this capacity will not be exceeded. You can define 2 types of capacity: e.g. one for weight (in pounds, kilos, tons) and one for volume (square meters, cube meters)

If you are using type restrictions on your visits, make sure you also define the same types for your vehicles in the type parameter. This value is an array of strings for multiple types. Vehicles without any type will still be able to serve the visits that have no type restrictions. Examples of type restrictions: "Technician", "Maintenance", ...

If a vehicle can do overtime, then the shift end constraint is not seen as hard and then it is possible that some orders are scheduled after shift end. overtime_end determines the maximum of overtime.

Breaks

A break can be scheduled between a certain break interval (breakstart and breakend) and can take a pre-defined amount of time (breakduration).

Introducing breaks will require more computation time, as they are seen as visits in the solution.

Linked orders

Orders that require to be executed at the same time are called Linked orders. A typical use case is that you would need 2 people to execute a job.

"linked_orders": [
  {
    "order1": "o1",
    "order2": "o2"
  }
]

Precedence relations

In PVRP, a time dependency (or precedence constraint) can be modelled for two orders. order1 is the name of the left order (must be executed first), referenced by the name provided in orders. order2 is the name of the right order (must be executed after order 1). min is the minimum time in days between order2 and order1 and max is the maximum time in days between order2 and order1.

"precedence_constraints": [
    {
      "order1" : "o1",
      "order2": "o2",
      "min": 1,
      "max": 5
    }
]

Options

Specific options can be added to guide the solving process.

Output

After checking that the status is solved, you can fetch the solution in the solution endpoint. The solution endpoint will return a list of visits per vehicle. Additionally, the score function will be presented as well as the unresolved constraints.

Score

Scoring an optimisation solution is done by dividing between soft and hard constraints.
Hard constraints are required constraints, while soft constraints force the solution in a certain direction as objective functions. For more information, see the section on What is Optimisation?

In the following example, the score is given for a simple optimisation question where you can see that there is no hard constraint violation (resulting in a hard score of 0) and the soft score results in 59 score points while aiming to reach to zero. This means that the solution is feasible according to the constraints.

{
	"score": {
		"hardScore": 0,
		"softScore": -59,
		"feasible": true
	}
}

Unresolved constraints

Some constraints cannot be resolved and are left violated. In the unresolved section in the solution, we show the constraints that are violated in a list with their respective score value.

"unresolved": [
    {
      "name": "Vehicle Capacity",
      "value": -4,
      "level": "HARD"
    }
  ]

Solution

The actual solution is a map of a list of visits to the vehicle name.

{
  "solution ": {
    "driver1": [
      {
        "location ": "loc0 ",
        "order ": null,
        "arrival ": 510,
        "finish ": 510,
        "wait ": 0,
        "drive ": 0,
        "distance ": 0
      },
      {
        "location ": "loc1 ",
        "order ": "order1 ",
        "arrival ": 504,
        "finish ": 534,
        "wait ": 0,
        "drive ": 24,
        "distance ": 24
      }
    ]
  }
}

Visit object

Example

{
	"solver": "VRP",
	"options": {
		"capacitySpread": true
	},
	"locations": [
		{
			"name": "loc0",
			"latitude": 51.12317420299715,
			"longitude": 5.281797793554478
		},
		{
			"name": "loc1",
			"latitude": 50.65329310557439,
			"longitude": 4.517600740757033
		},
		{
			"name": "loc2",
			"latitude": 51.20586951408341,
			"longitude": 5.050825570502835
		},
		{
			"name": "loc3",
			"latitude": 51.116048511299724,
			"longitude": 3.4438340449873435
		}
	],
	"fleet": [
		{
			"name": "drive John",
			"startlocation": "loc0",
			"shiftstart": 500,
			"shiftend": 1439,
			"type": [
				"Technician"
			]
		},
		{
			"name": "driver Joe",
			"startlocation": "loc2",
			"endlocation": "loc1",
			"shiftstart": 500,
			"shiftend": 900,
			"type": [
				"Maintenance"
			]
		}
	],
	"orders": [
		{
			"name": "order0",
			"location": "loc2",
			"windows": [
				{
					"starttime": 480,
					"endtime": 720,
					"hard": true
				},
				{
					"starttime": 780,
					"endtime": 1020,
					"hard": true
				}
			],
			"duration": 30,
			"type": [
				"Technician"
			]
		},
		{
			"name": "order1",
			"location": "loc1",
			"duration": 30
		},
		{
			"name": "order2",
			"location": "loc0",
			"demand": 2,
			"duration": 30
		},
		{
			"name": "order3",
			"location": "loc1",
			"demand": 2,
			"duration": 5
		}
	]
}