Include multiple shifts
You can combine multiple shift times for your fleet to manage and optimize vehicle routes more effectively when dealing with complex scenarios.
This feature allows specifying vehicle shifts and job time windows separately for different days within a single problem. For example, if you need to manage deliveries or pickups over a week, and your fleet has vehicles with varying shift times on different days, you can specify these constraints in one problem. This approach not only eliminates the need to create separate problems for each day but also enables optimal tour planning by maximizing fleet utilization. This approach is particularly beneficial for jobs that can be flexibly scheduled across different times or shifts, ensuring efficient and effective use of your resources.
Define multiple shifts in a problem
A common scenario for using multiple shifts involves having a long list of jobs with no time restrictions and a limited number of vehicles to serve them. In such cases, the vehicle can't complete the jobs within a single shift. By setting multiple shifts for your vehicles, you ensure that the vehicle serves the jobs over several shifts in the most efficient way.
Note
- Each vehicle's shift must have distinct start and end times without any overlap.
- Different shifts for a vehicle are distinguished by the
shiftIndexproperty in the tour. The vehicle ID remains the same for all shifts of a vehicle. For more information, see the API Reference.
Scenario: handle jobs with no time constraints
Consider a scenario in which you have 15 jobs with no time constraints, a single vehicle with a capacity of 10, and a shift length of only 3 hours per day. The vehicle can't complete all the jobs in one shift. To address this problem, you can specify multiple shifts for the vehicle, which allows that vehicle to complete the jobs over several days.
In this case, add dates to the vehicle's shift times, creating 4 shifts to cover the required work, as shown in the following example:
- Shift 1:
2021-10-23T09:00:00Z-2021-10-23T12:00:00Z - Shift 2:
2021-10-24T09:00:00Z-2021-10-24T12:00:00Z - Shift 3:
2021-10-25T09:00:00Z-2021-10-25T12:00:00Z - Shift 4:
2021-10-26T09:00:00Z-2021-10-26T12:00:00Z
Problem
The following problem demonstrates how you can specify multiple shifts for a vehicle, based on the previous sample scenario:
Click to expand/collapse the sample JSON
{
"fleet": {
"types": [
{
"id": "Vehicle_1",
"profile": "car",
"costs": {
"fixed": 10,
"distance": 0.002,
"time": 0.003
},
"shifts": [
{
"start": {
"time": "2021-10-23T09:00:00Z",
"location": {
"lat": 51.059188,
"lng": 13.540317
}
},
"end": {
"time": "2021-10-23T12:00:00Z",
"location": {
"lat": 51.059188,
"lng": 13.540317
}
}
},
{
"start": {
"time": "2021-10-24T09:00:00Z",
"location": {
"lat": 51.059188,
"lng": 13.540317
}
},
"end": {
"time": "2021-10-24T12:00:00Z",
"location": {
"lat": 51.059188,
"lng": 13.540317
}
}
},
{
"start": {
"time": "2021-10-25T09:00:00Z",
"location": {
"lat": 51.059188,
"lng": 13.540317
}
},
"end": {
"time": "2021-10-25T12:00:00Z",
"location": {
"lat": 51.059188,
"lng": 13.540317
}
}
},
{
"start": {
"time": "2021-10-26T09:00:00Z",
"location": {
"lat": 51.059188,
"lng": 13.540317
}
},
"end": {
"time": "2021-10-26T12:00:00Z",
"location": {
"lat": 51.059188,
"lng": 13.540317
}
}
}
],
"capacity": [
10
],
"amount": 1
}
],
"profiles": [
{
"type": "car",
"name": "car"
}
]
},
"plan": {
"jobs": [
{
"id": "Job_1",
"tasks": {
"pickups": [
{
"places": [
{
"location": {
"lat": 51.05238,
"lng": 13.74114
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_2",
"tasks": {
"pickups": [
{
"places": [
{
"location": {
"lat": 51.06099,
"lng": 13.75245
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_3",
"tasks": {
"pickups": [
{
"places": [
{
"location": {
"lat": 51.08511,
"lng": 13.76875
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_4",
"tasks": {
"pickups": [
{
"places": [
{
"location": {
"lat": 51.1323847,
"lng": 13.7779515
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_5",
"tasks": {
"pickups": [
{
"places": [
{
"location": {
"lat": 51.11716,
"lng": 13.73054
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_6",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 51.12308,
"lng": 13.76406
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_7",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 51.08588,
"lng": 13.72637
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_8",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 51.08588,
"lng": 13.72637
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_9",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 51.08588,
"lng": 13.72637
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_10",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 51.18588,
"lng": 13.52637
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_11",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 51.10588,
"lng": 13.79637
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_12",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 51.01588,
"lng": 13.52637
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_13",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 51.08588,
"lng": 13.62637
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_14",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 51.00088,
"lng": 13.02637
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_15",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 51.06866,
"lng": 13.77273
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
}
]
}
}Solution
The following figure provides a visual breakdown of the resulting solution:
As the solution demonstrates, the optimization algorithm distributed jobs across the vehicle's four shifts as follows:
- Five jobs on October 23
- Five jobs on October 24
- One job on October 25
- Four jobs on October 26
This way, the vehicle was able to complete all its assigned jobs in an efficient way, without violating the shift time windows.
The following section contains the full solution JSON:
Click to expand/collapse the sample JSON
{
"statistic": {
"cost": 673.386,
"distance": 262603,
"duration": 36060,
"times": {
"driving": 15810,
"serving": 20250,
"waiting": 0,
"break": 0
}
},
"tours": [
{
"vehicleId": "Vehicle_1_1",
"typeId": "Vehicle_1",
"stops": [
{
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"time": {
"arrival": "2021-10-24T09:00:00Z",
"departure": "2021-10-24T09:00:00Z"
},
"load": [
2
],
"activities": [
{
"jobId": "departure",
"type": "departure"
}
],
"distance": 0
},
{
"location": {
"lat": 51.05238,
"lng": 13.74114
},
"time": {
"arrival": "2021-10-24T09:23:14Z",
"departure": "2021-10-24T09:45:44Z"
},
"load": [
3
],
"activities": [
{
"jobId": "Job_1",
"type": "pickup"
}
],
"distance": 22782
},
{
"location": {
"lat": 51.06099,
"lng": 13.75245
},
"time": {
"arrival": "2021-10-24T09:50:59Z",
"departure": "2021-10-24T10:13:29Z"
},
"load": [
4
],
"activities": [
{
"jobId": "Job_2",
"type": "pickup"
}
],
"distance": 24694
},
{
"location": {
"lat": 51.06866,
"lng": 13.77273
},
"time": {
"arrival": "2021-10-24T10:16:40Z",
"departure": "2021-10-24T10:39:10Z"
},
"load": [
3
],
"activities": [
{
"jobId": "Job_15",
"type": "delivery"
}
],
"distance": 27864
},
{
"location": {
"lat": 51.08511,
"lng": 13.76875
},
"time": {
"arrival": "2021-10-24T10:45:22Z",
"departure": "2021-10-24T11:07:52Z"
},
"load": [
4
],
"activities": [
{
"jobId": "Job_3",
"type": "pickup"
}
],
"distance": 31892
},
{
"location": {
"lat": 51.10588,
"lng": 13.79637
},
"time": {
"arrival": "2021-10-24T11:13:48Z",
"departure": "2021-10-24T11:36:18Z"
},
"load": [
3
],
"activities": [
{
"jobId": "Job_11",
"type": "delivery"
}
],
"distance": 35237
},
{
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"time": {
"arrival": "2021-10-24T11:57:06Z",
"departure": "2021-10-24T11:57:06Z"
},
"load": [
0
],
"activities": [
{
"jobId": "arrival",
"type": "arrival"
}
],
"distance": 56742
}
],
"statistic": {
"cost": 152.36999999999998,
"distance": 55246,
"duration": 10626,
"times": {
"driving": 3876,
"serving": 6750,
"waiting": 0,
"break": 0
}
},
"shiftIndex": 1
},
{
"vehicleId": "Vehicle_1_1",
"typeId": "Vehicle_1",
"stops": [
{
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"time": {
"arrival": "2021-10-26T09:00:00Z",
"departure": "2021-10-26T09:00:00Z"
},
"load": [
2
],
"activities": [
{
"jobId": "departure",
"type": "departure"
}
],
"distance": 0
},
{
"location": {
"lat": 51.18588,
"lng": 13.52637
},
"time": {
"arrival": "2021-10-26T09:28:01Z",
"departure": "2021-10-26T09:50:31Z"
},
"load": [
1
],
"activities": [
{
"jobId": "Job_10",
"type": "delivery"
}
],
"distance": 9299
},
{
"location": {
"lat": 51.1323847,
"lng": 13.7779515
},
"time": {
"arrival": "2021-10-26T10:16:25Z",
"departure": "2021-10-26T10:38:55Z"
},
"load": [
2
],
"activities": [
{
"jobId": "Job_4",
"type": "pickup"
}
],
"distance": 22650
},
{
"location": {
"lat": 51.12308,
"lng": 13.76406
},
"time": {
"arrival": "2021-10-26T10:43:05Z",
"departure": "2021-10-26T11:05:35Z"
},
"load": [
1
],
"activities": [
{
"jobId": "Job_6",
"type": "delivery"
}
],
"distance": 27842
},
{
"location": {
"lat": 51.11716,
"lng": 13.73054
},
"time": {
"arrival": "2021-10-26T11:11:20Z",
"departure": "2021-10-26T11:33:50Z"
},
"load": [
2
],
"activities": [
{
"jobId": "Job_5",
"type": "pickup"
}
],
"distance": 48395
},
{
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"time": {
"arrival": "2021-10-26T11:49:06Z",
"departure": "2021-10-26T11:49:06Z"
},
"load": [
0
],
"activities": [
{
"jobId": "arrival",
"type": "arrival"
}
],
"distance": 68746
}
],
"statistic": {
"cost": 180.524,
"distance": 70043,
"duration": 10146,
"times": {
"driving": 4746,
"serving": 5400,
"waiting": 0,
"break": 0
}
},
"shiftIndex": 3
},
{
"vehicleId": "Vehicle_1_1",
"typeId": "Vehicle_1",
"stops": [
{
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"time": {
"arrival": "2021-10-23T09:00:00Z",
"departure": "2021-10-23T09:00:00Z"
},
"load": [
5
],
"activities": [
{
"jobId": "departure",
"type": "departure"
}
],
"distance": 0
},
{
"location": {
"lat": 51.08588,
"lng": 13.62637
},
"time": {
"arrival": "2021-10-23T09:13:53Z",
"departure": "2021-10-23T09:36:23Z"
},
"load": [
4
],
"activities": [
{
"jobId": "Job_13",
"type": "delivery"
}
],
"distance": 18700
},
{
"location": {
"lat": 51.08588,
"lng": 13.72637
},
"time": {
"arrival": "2021-10-23T09:51:23Z",
"departure": "2021-10-23T10:58:53Z"
},
"load": [
1
],
"activities": [
{
"jobId": "Job_9",
"type": "delivery",
"location": {
"lat": 51.08588,
"lng": 13.72637
},
"time": {
"start": "2021-10-23T09:51:23Z",
"end": "2021-10-23T10:13:53Z"
}
},
{
"jobId": "Job_7",
"type": "delivery",
"location": {
"lat": 51.08588,
"lng": 13.72637
},
"time": {
"start": "2021-10-23T10:13:53Z",
"end": "2021-10-23T10:36:23Z"
}
},
{
"jobId": "Job_8",
"type": "delivery",
"location": {
"lat": 51.08588,
"lng": 13.72637
},
"time": {
"start": "2021-10-23T10:36:23Z",
"end": "2021-10-23T10:58:53Z"
}
}
],
"distance": 22771
},
{
"location": {
"lat": 51.01588,
"lng": 13.52637
},
"time": {
"arrival": "2021-10-23T11:20:08Z",
"departure": "2021-10-23T11:42:38Z"
},
"load": [
0
],
"activities": [
{
"jobId": "Job_12",
"type": "delivery"
}
],
"distance": 25067
},
{
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"time": {
"arrival": "2021-10-23T11:55:00Z",
"departure": "2021-10-23T11:55:00Z"
},
"load": [
0
],
"activities": [
{
"jobId": "arrival",
"type": "arrival"
}
],
"distance": 44098
}
],
"statistic": {
"cost": 148.944,
"distance": 53722,
"duration": 10500,
"times": {
"driving": 3750,
"serving": 6750,
"waiting": 0,
"break": 0
}
},
"shiftIndex": 0
},
{
"vehicleId": "Vehicle_1_1",
"typeId": "Vehicle_1",
"stops": [
{
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"time": {
"arrival": "2021-10-25T09:00:00Z",
"departure": "2021-10-25T09:00:00Z"
},
"load": [
1
],
"activities": [
{
"jobId": "departure",
"type": "departure"
}
],
"distance": 0
},
{
"location": {
"lat": 51.00088,
"lng": 13.02637
},
"time": {
"arrival": "2021-10-25T09:28:45Z",
"departure": "2021-10-25T09:51:15Z"
},
"load": [
0
],
"activities": [
{
"jobId": "Job_14",
"type": "delivery"
}
],
"distance": 41711
},
{
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"time": {
"arrival": "2021-10-25T10:19:48Z",
"departure": "2021-10-25T10:19:48Z"
},
"load": [
0
],
"activities": [
{
"jobId": "arrival",
"type": "arrival"
}
],
"distance": 83583
}
],
"statistic": {
"cost": 191.548,
"distance": 83592,
"duration": 4788,
"times": {
"driving": 3438,
"serving": 1350,
"waiting": 0,
"break": 0
}
},
"shiftIndex": 2
}
]
}Scenario: handle jobs with specific time constraints
For jobs with specific time constraints, you add time windows to ensure they are scheduled appropriately. For example, if a job can only be delivered on October 23, you would add a time window for that job on that date, within the corresponding shift time.
You can schedule jobs without specific time constraints on any day by not specifying a time window. The optimization algorithm schedules such jobs in the way that is most efficient for the overall tour.
Consider a scenario where you need to serve 10 jobs using a vehicle with different shift times over three days. Five of these jobs have specific time constraints, while the other five do not.
Vehicle availability:
- Shift 1: from 8:00 to 14:00 on October 23
- Shift 2: from 9:00 to 15:00 on October 24
- Shift 3: from 10:00 to 21:00 on October 25
Jobs with Time Windows:
Job 1: Must be started on October 23 between 9:00 and 12:00Job 2: Must be started on October 23 between 12:00 and 14:00Job 3: Must be started on October 24 between 10:00 and 12:00Job 4: Must be started on October 25 between 11:00 and 13:00Job 5: Must be started on October 25 between 15:00 and 18:00
Jobs without Time Windows:
Job 6 through Job 10 can be completed on any day.
Problem
The following section contains a problem JSON reflecting the previously mentioned constraints:
Click to expand/collapse the sample JSON
{
"fleet": {
"types": [
{
"id": "Vehicle_1",
"profile": "car",
"costs": {
"fixed": 10,
"distance": 0.002,
"time": 0.003
},
"shifts": [
{
"start": {
"time": "2021-10-23T08:00:00Z",
"location": {
"lat": 51.059188,
"lng": 13.540317
}
},
"end": {
"time": "2021-10-23T14:00:00Z",
"location": {
"lat": 51.059188,
"lng": 13.540317
}
}
},
{
"start": {
"time": "2021-10-24T09:00:00Z",
"location": {
"lat": 51.059188,
"lng": 13.540317
}
},
"end": {
"time": "2021-10-24T15:00:00Z",
"location": {
"lat": 51.059188,
"lng": 13.540317
}
}
},
{
"start": {
"time": "2021-10-25T10:00:00Z",
"location": {
"lat": 51.059188,
"lng": 13.540317
}
},
"end": {
"time": "2021-10-25T21:00:00Z",
"location": {
"lat": 51.059188,
"lng": 13.540317
}
}
}
],
"capacity": [
10
],
"amount": 1
}
],
"profiles": [
{
"type": "car",
"name": "car"
}
]
},
"plan": {
"jobs": [
{
"id": "Job_1",
"tasks": {
"pickups": [
{
"places": [
{
"times": [
[
"2021-10-23T09:00:00Z",
"2021-10-23T12:00:00Z"
]
],
"location": {
"lat": 51.05238,
"lng": 13.74114
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_2",
"tasks": {
"pickups": [
{
"places": [
{
"times": [
[
"2021-10-23T12:00:00Z",
"2021-10-23T14:00:00Z"
]
],
"location": {
"lat": 51.06099,
"lng": 13.75245
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_3",
"tasks": {
"pickups": [
{
"places": [
{
"times": [
[
"2021-10-24T10:00:00Z",
"2021-10-24T12:00:00Z"
]
],
"location": {
"lat": 51.08511,
"lng": 13.76875
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_4",
"tasks": {
"pickups": [
{
"places": [
{
"times": [
[
"2021-10-25T11:00:00Z",
"2021-10-25T13:00:00Z"
]
],
"location": {
"lat": 51.1323847,
"lng": 13.7779515
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_5",
"tasks": {
"pickups": [
{
"places": [
{
"times": [
[
"2021-10-25T15:00:00Z",
"2021-10-25T18:00:00Z"
]
],
"location": {
"lat": 51.11716,
"lng": 13.73054
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_6",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 51.12308,
"lng": 13.76406
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_7",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 51.08588,
"lng": 13.72637
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_8",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 51.08588,
"lng": 13.72637
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_9",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 51.08588,
"lng": 13.72637
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
},
{
"id": "Job_10",
"tasks": {
"deliveries": [
{
"places": [
{
"location": {
"lat": 51.06866,
"lng": 13.77273
},
"duration": 1350
}
],
"demand": [
1
]
}
]
}
}
]
}
}Solution
The following figure provides a visual breakdown of the resulting solution, highlighting the jobs constrained by time windows:
The optimization algorithm successfully scheduled all time-constrained jobs within their specified time windows. The remaining jobs, which were not associated with any time windows, were scheduled flexibly within the shifts to maximize overall tour efficiency.
The following section contains the solution JSON, for reference:
Click to expand/collapse the sample JSON
{
"statistic": {
"cost": 390.953,
"distance": 141352,
"duration": 26083,
"times": {
"driving": 8644,
"serving": 13500,
"waiting": 3939,
"stopping": 0,
"break": 0
}
},
"tours": [
{
"vehicleId": "Vehicle_1_1",
"typeId": "Vehicle_1",
"stops": [
{
"time": {
"arrival": "2021-10-24T09:00:00Z",
"departure": "2021-10-24T09:00:00Z"
},
"load": [
4
],
"activities": [
{
"jobId": "departure",
"type": "departure",
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"time": {
"start": "2021-10-24T09:00:00Z",
"end": "2021-10-24T09:00:00Z"
}
}
],
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"distance": 0
},
{
"time": {
"arrival": "2021-10-24T09:15:25Z",
"departure": "2021-10-24T10:22:55Z"
},
"load": [
1
],
"activities": [
{
"jobId": "Job_8",
"type": "delivery",
"location": {
"lat": 51.08588,
"lng": 13.72637
},
"time": {
"start": "2021-10-24T09:15:25Z",
"end": "2021-10-24T09:37:55Z"
}
},
{
"jobId": "Job_9",
"type": "delivery",
"location": {
"lat": 51.08588,
"lng": 13.72637
},
"time": {
"start": "2021-10-24T09:37:55Z",
"end": "2021-10-24T10:00:25Z"
}
},
{
"jobId": "Job_7",
"type": "delivery",
"location": {
"lat": 51.08588,
"lng": 13.72637
},
"time": {
"start": "2021-10-24T10:00:25Z",
"end": "2021-10-24T10:22:55Z"
}
}
],
"location": {
"lat": 51.08588,
"lng": 13.72637
},
"distance": 18695
},
{
"time": {
"arrival": "2021-10-24T10:31:12Z",
"departure": "2021-10-24T10:53:42Z"
},
"load": [
0
],
"activities": [
{
"jobId": "Job_10",
"type": "delivery",
"location": {
"lat": 51.06866,
"lng": 13.77273
},
"time": {
"start": "2021-10-24T10:31:12Z",
"end": "2021-10-24T10:53:42Z"
}
}
],
"location": {
"lat": 51.06866,
"lng": 13.77273
},
"distance": 23119
},
{
"time": {
"arrival": "2021-10-24T10:59:20Z",
"departure": "2021-10-24T11:21:50Z"
},
"load": [
1
],
"activities": [
{
"jobId": "Job_3",
"type": "pickup",
"location": {
"lat": 51.08511,
"lng": 13.76875
},
"time": {
"start": "2021-10-24T10:59:20Z",
"end": "2021-10-24T11:21:50Z"
}
}
],
"location": {
"lat": 51.08511,
"lng": 13.76875
},
"distance": 26548
},
{
"time": {
"arrival": "2021-10-24T11:44:40Z",
"departure": "2021-10-24T11:44:40Z"
},
"load": [
0
],
"activities": [
{
"jobId": "arrival",
"type": "arrival",
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"time": {
"start": "2021-10-24T11:44:40Z",
"end": "2021-10-24T11:44:40Z"
}
}
],
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"distance": 50767
}
],
"statistic": {
"cost": 141.174,
"distance": 50767,
"duration": 9880,
"times": {
"driving": 3130,
"serving": 6750,
"waiting": 0,
"stopping": 0,
"break": 0
}
},
"shiftIndex": 1
},
{
"vehicleId": "Vehicle_1_1",
"typeId": "Vehicle_1",
"stops": [
{
"time": {
"arrival": "2021-10-25T10:00:00Z",
"departure": "2021-10-25T12:40:29Z"
},
"load": [
1
],
"activities": [
{
"jobId": "departure",
"type": "departure",
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"time": {
"start": "2021-10-25T10:00:00Z",
"end": "2021-10-25T12:40:29Z"
}
}
],
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"distance": 0
},
{
"time": {
"arrival": "2021-10-25T13:00:00Z",
"departure": "2021-10-25T13:22:30Z"
},
"load": [
2
],
"activities": [
{
"jobId": "Job_4",
"type": "pickup",
"location": {
"lat": 51.1323847,
"lng": 13.7779515
},
"time": {
"start": "2021-10-25T13:00:00Z",
"end": "2021-10-25T13:22:30Z"
}
}
],
"location": {
"lat": 51.1323847,
"lng": 13.7779515
},
"distance": 23558
},
{
"time": {
"arrival": "2021-10-25T13:26:28Z",
"departure": "2021-10-25T13:48:58Z"
},
"load": [
1
],
"activities": [
{
"jobId": "Job_6",
"type": "delivery",
"location": {
"lat": 51.12308,
"lng": 13.76406
},
"time": {
"start": "2021-10-25T13:26:28Z",
"end": "2021-10-25T13:48:58Z"
}
}
],
"location": {
"lat": 51.12308,
"lng": 13.76406
},
"distance": 25366
},
{
"time": {
"arrival": "2021-10-25T13:54:21Z",
"departure": "2021-10-25T15:22:30Z"
},
"load": [
2
],
"activities": [
{
"jobId": "Job_5",
"type": "pickup",
"location": {
"lat": 51.11716,
"lng": 13.73054
},
"time": {
"start": "2021-10-25T15:00:00Z",
"end": "2021-10-25T15:22:30Z"
}
}
],
"location": {
"lat": 51.11716,
"lng": 13.73054
},
"distance": 29456
},
{
"time": {
"arrival": "2021-10-25T15:37:35Z",
"departure": "2021-10-25T15:37:35Z"
},
"load": [
0
],
"activities": [
{
"jobId": "arrival",
"type": "arrival",
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"time": {
"start": "2021-10-25T15:37:35Z",
"end": "2021-10-25T15:37:35Z"
}
}
],
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"distance": 49111
}
],
"statistic": {
"cost": 140.10000000000002,
"distance": 49111,
"duration": 10626,
"times": {
"driving": 2637,
"serving": 4050,
"waiting": 3939,
"stopping": 0,
"break": 0
}
},
"shiftIndex": 2
},
{
"vehicleId": "Vehicle_1_1",
"typeId": "Vehicle_1",
"stops": [
{
"time": {
"arrival": "2021-10-23T08:00:00Z",
"departure": "2021-10-23T11:10:08Z"
},
"load": [
0
],
"activities": [
{
"jobId": "departure",
"type": "departure",
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"time": {
"start": "2021-10-23T08:00:00Z",
"end": "2021-10-23T11:10:08Z"
}
}
],
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"distance": 0
},
{
"time": {
"arrival": "2021-10-23T11:31:57Z",
"departure": "2021-10-23T11:54:27Z"
},
"load": [
1
],
"activities": [
{
"jobId": "Job_1",
"type": "pickup",
"location": {
"lat": 51.05238,
"lng": 13.74114
},
"time": {
"start": "2021-10-23T11:31:57Z",
"end": "2021-10-23T11:54:27Z"
}
}
],
"location": {
"lat": 51.05238,
"lng": 13.74114
},
"distance": 19038
},
{
"time": {
"arrival": "2021-10-23T12:00:00Z",
"departure": "2021-10-23T12:22:30Z"
},
"load": [
2
],
"activities": [
{
"jobId": "Job_2",
"type": "pickup",
"location": {
"lat": 51.06099,
"lng": 13.75245
},
"time": {
"start": "2021-10-23T12:00:00Z",
"end": "2021-10-23T12:22:30Z"
}
}
],
"location": {
"lat": 51.06099,
"lng": 13.75245
},
"distance": 21408
},
{
"time": {
"arrival": "2021-10-23T12:43:05Z",
"departure": "2021-10-23T12:43:05Z"
},
"load": [
0
],
"activities": [
{
"jobId": "arrival",
"type": "arrival",
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"time": {
"start": "2021-10-23T12:43:05Z",
"end": "2021-10-23T12:43:05Z"
}
}
],
"location": {
"lat": 51.059188,
"lng": 13.540317
},
"distance": 41474
}
],
"statistic": {
"cost": 109.679,
"distance": 41474,
"duration": 5577,
"times": {
"driving": 2877,
"serving": 2700,
"waiting": 0,
"stopping": 0,
"break": 0
}
},
"shiftIndex": 0
}
]
}Next steps
- For more information in time windows, see Limit the start time of activities through time windows.
- For more information on VRP problems, see Problem and Follow best practices for problem formulation.