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Open VRP
It occurs very often when delivery companies use vehicles that are not on the company staff which implies that those vehicles may not return to the depot, or may work on their specific shifts as contractors. Such a type of problem is called Open VRP. In the Open VRP, contractors who are not employees of the delivery company use their own vehicles and do not return to the depot after servicing the last customer. The Open VRP may be encountered in real-life as home delivery of any products or services, food delivery, etc.
The Open VRP is a variance of the vehicle routing problem that has a unique character which is its open path form. This means that the vehicles are not required to return to the depot after completing service, therefore the key constraints for such problems are pickup and delivery locations and vehicles time shifts.
Let’s consider a situation when a delivery company hires two vehicles for delivering any products to the customers. Those vehicles do not belong to the company and the drivers are contractors. The company does not even have a depot where those vehicles start their route, but it has a warehouse where they need to pick up the goods that they will need to be delivered.
Thus, that warehouse location will be the starting location for both vehicles. Considering that after executing the last delivery the vehicles don’t need to return to the depot, then they will end their tours on the last delivery. Also in this situation, we will need to set the shiftTime for the vehicles’ limits to control the drivers working time.
Problem
With all the constraints above, the problem for this specific open VRP will look as follows:
{
"fleet": {
"types": [
{
"id": "048d43e6e61c",
"profile": "car_1",
"costs": {
"fixed": 10.0,
"distance": 0.002,
"time": 0.01
},
"shifts": [
{
"start": {
"time": "2021-07-13T08:20:00Z",
"location": {
"lat": 52.530971,
"lng": 13.384915
}
}
}
],
"capacity": [
100
],
"limits": {
"shiftTime": 43200
},
"amount": 2
}
],
"profiles": [
{
"type": "car",
"name": "car_1"
}
]
},
"plan": {
"jobs": [
{
"id": "job_1",
"tasks": {
"pickups": [
{
"places": [
{
"location": {
"lat": 52.513365341037485,
"lng": 13.325000854641972
},
"duration": 780
}
],
"demand": [
40
]
}
]
}
},
{
"id": "job_2",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 52.7478839710114,
"lng": 13.44595304182502
},
"duration": 120
}
],
"demand": [
5
]
}
]
}
},
{
"id": "job_3",
"tasks": {
"pickups": [
{
"places": [
{
"location": {
"lat": 52.44003237354213,
"lng": 13.422125667246638
},
"duration": 360
}
],
"demand": [
10
]
}
]
}
},
{
"id": "job_4",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 52.55783238016377,
"lng": 13.304436007504957
},
"duration": 720
}
],
"demand": [
10
]
}
]
}
},
{
"id": "job_5",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 52.60592698633012,
"lng": 13.360660447014226
},
"duration": 240
}
],
"demand": [
5
]
}
]
}
},
{
"id": "job_6",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 52.547429320898374,
"lng": 13.303190999428224
},
"duration": 780
}
],
"demand": [
15
]
}
]
}
}
]
}
}
Solution
The solution for such problem will look as follows:
{
"statistic": {
"cost": 267.092,
"distance": 73626,
"duration": 10984,
"times": {
"driving": 7984,
"serving": 3000,
"waiting": 0,
"break": 0
}
},
"tours": [
{
"vehicleId": "048d43e6e61c_2",
"typeId": "048d43e6e61c",
"stops": [
{
"location": {
"lat": 52.530971,
"lng": 13.384915
},
"time": {
"arrival": "2021-07-13T08:20:00Z",
"departure": "2021-07-13T08:20:00Z"
},
"load": [
35
],
"activities": [
{
"jobId": "departure",
"type": "departure"
}
]
},
{
"location": {
"lat": 52.44003237354213,
"lng": 13.422125667246638
},
"time": {
"arrival": "2021-07-13T08:48:16Z",
"departure": "2021-07-13T08:54:16Z"
},
"load": [
45
],
"activities": [
{
"jobId": "job_3",
"type": "pickup"
}
]
},
{
"location": {
"lat": 52.513365341037485,
"lng": 13.325000854641972
},
"time": {
"arrival": "2021-07-13T09:19:01Z",
"departure": "2021-07-13T09:32:01Z"
},
"load": [
85
],
"activities": [
{
"jobId": "job_1",
"type": "pickup"
}
]
},
{
"location": {
"lat": 52.547429320898374,
"lng": 13.303190999428224
},
"time": {
"arrival": "2021-07-13T09:48:41Z",
"departure": "2021-07-13T10:01:41Z"
},
"load": [
70
],
"activities": [
{
"jobId": "job_6",
"type": "delivery"
}
]
},
{
"location": {
"lat": 52.55783238016377,
"lng": 13.304436007504956
},
"time": {
"arrival": "2021-07-13T10:11:56Z",
"departure": "2021-07-13T10:23:56Z"
},
"load": [
60
],
"activities": [
{
"jobId": "job_4",
"type": "delivery"
}
]
},
{
"location": {
"lat": 52.60592698633012,
"lng": 13.360660447014226
},
"time": {
"arrival": "2021-07-13T10:40:28Z",
"departure": "2021-07-13T10:44:28Z"
},
"load": [
55
],
"activities": [
{
"jobId": "job_5",
"type": "delivery"
}
]
},
{
"location": {
"lat": 52.7478839710114,
"lng": 13.44595304182502
},
"time": {
"arrival": "2021-07-13T11:21:04Z",
"departure": "2021-07-13T11:23:04Z"
},
"load": [
50
],
"activities": [
{
"jobId": "job_2",
"type": "delivery"
}
]
}
],
"statistic": {
"cost": 267.092,
"distance": 73626,
"duration": 10984,
"times": {
"driving": 7984,
"serving": 3000,
"waiting": 0,
"break": 0
}
}
}
]
}
From this solution, we can see the statistic of the vehicles' tours, including the total cost, duration, distance, and time spent for driving and serving with all the locations visited regarding the constraints.
