AI-generated Key Takeaways
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loadDemandsspecify the load a shipment requires, whileloadLimitsspecify the maximum load a vehicle can handle. -
Both
loadDemandsandloadLimitsare defined using types, which can represent weight, volume, or other custom categories. -
Shipments are assigned to vehicles based on their load demands and the vehicles' load limits, ensuring efficient capacity utilization.
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The
OptimizeToursResponseprovides details on vehicle loads at different points in the route, reflecting load transfers during pickups and deliveries. -
In addition to hard limits, soft load limits can be used to model preferences or costs associated with exceeding a preferred load threshold.
This example shows how to use loadDemands and loadLimits to manage
vehicle capacity constraints in a Route Optimization API request.
For a complete conceptual overview, see the Load demands and limits key concepts document.
Example request
The following example demonstrates a scenario where a single vehicle with a load limit must deliver three shipments of different weights.
This example request contains the following load-related parameters:
shipments[0]with a load demandamountof 50weightKg.shipments[1]with a load demandamountof 10weightKg.shipments[2]with a load demandamountof 80weightKg.vehicles[0]with a load limitmaxLoadof 100weightKg.
See an example request with load demands and limits
{ "populatePolylines": false, "populateTransitionPolylines": false, "model": { "globalStartTime": "2023-01-13T16:00:00Z", "globalEndTime": "2023-01-14T16:00:00Z", "shipments": [ { "deliveries": [ { "arrivalLocation": { "latitude": 37.789456, "longitude": -122.390192 }, "duration": "250s" } ], "pickups": [ { "arrivalLocation": { "latitude": 37.794465, "longitude": -122.394839 }, "duration": "150s" } ], "penaltyCost": 100.0, "loadDemands": { "weightKg": { "amount": "50" } } }, { "deliveries": [ { "arrivalLocation": { "latitude": 37.789116, "longitude": -122.395080 }, "duration": "250s" } ], "pickups": [ { "arrivalLocation": { "latitude": 37.794465, "longitude": -122.394839 }, "duration": "150s" } ], "penaltyCost": 15.0, "loadDemands": { "weightKg": { "amount": "10" } } }, { "deliveries": [ { "arrivalLocation": { "latitude": 37.795242, "longitude": -122.399347 }, "duration": "250s" } ], "pickups": [ { "arrivalLocation": { "latitude": 37.794465, "longitude": -122.394839 }, "duration": "150s" } ], "penaltyCost": 50.0, "loadDemands": { "weightKg": { "amount": "80" } } } ], "vehicles": [ { "endLocation": { "latitude": 37.794465, "longitude": -122.394839 }, "startLocation": { "latitude": 37.794465, "longitude": -122.394839 }, "costPerHour": 40.0, "costPerKilometer": 10.0, "loadLimits": { "weightKg": { "maxLoad": "100" } } } ] } }
Example response
The response shows the optimized route for the vehicle. Because the total load
of all shipments exceeds the vehicle's capacity, the optimizer creates a
sequence of pickups and deliveries to ensure the loadLimits are not
violated.
See a response to the request with load demands and limits
{ "routes": [ { "vehicleStartTime": "2023-01-13T16:00:00Z", "vehicleEndTime": "2023-01-13T16:43:27Z", "visits": [ { "isPickup": true, "startTime": "2023-01-13T16:00:00Z", "detour": "0s", "loadDemands": { "weightKg": { "amount": "50" } } }, { "shipmentIndex": 1, "isPickup": true, "startTime": "2023-01-13T16:02:30Z", "detour": "150s", "loadDemands": { "weightKg": { "amount": "10" } } }, { "startTime": "2023-01-13T16:08:55Z", "detour": "150s", "loadDemands": { "weightKg": { "amount": "-50" } } }, { "shipmentIndex": 1, "startTime": "2023-01-13T16:16:37Z", "detour": "343s", "loadDemands": { "weightKg": { "amount": "-10" } } }, { "shipmentIndex": 2, "isPickup": true, "startTime": "2023-01-13T16:27:07Z", "detour": "1627s", "loadDemands": { "weightKg": { "amount": "80" } } }, { "shipmentIndex": 2, "startTime": "2023-01-13T16:36:26Z", "detour": "0s", "loadDemands": { "weightKg": { "amount": "-80" } } } ], "transitions": [ { "travelDuration": "0s", "waitDuration": "0s", "totalDuration": "0s", "startTime": "2023-01-13T16:00:00Z", "vehicleLoads": { "weightKg": {} } }, { "travelDuration": "0s", "waitDuration": "0s", "totalDuration": "0s", "startTime": "2023-01-13T16:02:30Z", "vehicleLoads": { "weightKg": { "amount": "50" } } }, { "travelDuration": "235s", "travelDistanceMeters": 795, "waitDuration": "0s", "totalDuration": "235s", "startTime": "2023-01-13T16:05:00Z", "vehicleLoads": { "weightKg": { "amount": "60" } } }, { "travelDuration": "212s", "travelDistanceMeters": 791, "waitDuration": "0s", "totalDuration": "212s", "startTime": "2023-01-13T16:13:05Z", "vehicleLoads": { "weightKg": { "amount": "10" } } }, { "travelDuration": "380s", "travelDistanceMeters": 1190, "waitDuration": "0s", "totalDuration": "380s", "startTime": "2023-01-13T16:20:47Z", "vehicleLoads": { "weightKg": {} } }, { "travelDuration": "409s", "travelDistanceMeters": 1371, "waitDuration": "0s", "totalDuration": "409s", "startTime": "2023-01-13T16:29:37Z", "vehicleLoads": { "weightKg": { "amount": "80" } } }, { "travelDuration": "171s", "travelDistanceMeters": 665, "waitDuration": "0s", "totalDuration": "171s", "startTime": "2023-01-13T16:40:36Z", "vehicleLoads": { "weightKg": {} } } ], "metrics": { "performedShipmentCount": 3, "travelDuration": "1407s", "waitDuration": "0s", "delayDuration": "0s", "breakDuration": "0s", "visitDuration": "1200s", "totalDuration": "2607s", "travelDistanceMeters": 4812, "maxLoads": { "weightKg": { "amount": "80" } } }, "routeCosts": { "model.vehicles.cost_per_kilometer": 48.12, "model.vehicles.cost_per_hour": 28.966666666666665 }, "routeTotalCost": 77.086666666666659 } ], "metrics": { "aggregatedRouteMetrics": { "performedShipmentCount": 3, "travelDuration": "1407s", "waitDuration": "0s", "delayDuration": "0s", "breakDuration": "0s", "visitDuration": "1200s", "totalDuration": "2607s", "travelDistanceMeters": 4812, "maxLoads": { "weightKg": { "amount": "80" } } }, "usedVehicleCount": 1, "earliestVehicleStartTime": "2023-01-13T16:00:00Z", "latestVehicleEndTime": "2023-01-13T16:43:27Z", "totalCost": 77.086666666666659, "costs": { "model.vehicles.cost_per_hour": 28.966666666666665, "model.vehicles.cost_per_kilometer": 48.12 } } }
Because the combined loadDemands of the three shipments (50 + 10 + 80 =
140) exceeds the vehicle's loadLimits (100), the vehicle can't pick up
all shipments at once. The optimizer only considers routes where shipment[0]
and shipment[2] are not in the vehicle at the same time, since these shipments
are the ones that exceed the vehicle's load limit with their combined weights.
The route has the following visits to not exceed the vehicle's load
limit:
shipment[0]is picked upshipment[1]is picked upshipment[0]is deliveredshipment[1]is deliveredshipment[2]is picked upshipment[2]is delivered
The vehicle's load changes throughout the route, which you can observe in the
transitions array. For example, transitions[2] shows the vehicle
carrying a load of 60 weightKg after picking up the first two shipments (50 +
10).
The maxLoads property in metrics shows that the maximum load
carried at any point in the route was 80 weightKg, confirming that the
solution successfully stayed within the vehicle's 100 weightKg limit.
Soft load limits
The following example shows how to use a soft load limit to optimize a route with multiple vehicles. The solution splits the shipments between the two vehicles to avoid the cost penalty for exceeding the vehicle's soft load limit.
Example request
This request now includes three delivery-only shipments and two vehicles with
the same loadLimits and softMaxLoad.
The key parameters for this example are:
- The three shipments have a combined
loadDemandsof 140weightKg(50- 60 + 30).
- There are two vehicles available with a
softMaxLoadof "100"weightKgand acostPerUnitAboveSoftMaxof 5.0.
See an example request with a soft load limit
{ "populatePolylines": false, "populateTransitionPolylines": false, "model": { "globalStartTime": "2023-01-13T16:00:00Z", "globalEndTime": "2023-01-14T16:00:00Z", "shipments": [ { "deliveries": [ { "arrivalLocation": { "latitude": 37.789456, "longitude": -122.390192 }, "duration": "250s" } ], "loadDemands": { "weightKg": { "amount": "50" } } }, { "deliveries": [ { "arrivalLocation": { "latitude": 37.789116, "longitude": -122.395080 }, "duration": "250s" } ], "loadDemands": { "weightKg": { "amount": "60" } } }, { "deliveries": [ { "arrivalLocation": { "latitude": 37.795242, "longitude": -122.399347 }, "duration": "250s" } ], "loadDemands": { "weightKg": { "amount": "30" } } } ], "vehicles": [ { "endLocation": { "latitude": 37.794465, "longitude": -122.394839 }, "startLocation": { "latitude": 37.794465, "longitude": -122.394839 }, "costPerHour": 40.0, "costPerKilometer": 10.0, "loadLimits": { "weightKg": { "maxLoad": "150", "softMaxLoad": "100", "costPerUnitAboveSoftMax": 5.0 } } }, { "endLocation": { "latitude": 37.794465, "longitude": -122.394839 }, "startLocation": { "latitude": 37.794465, "longitude": -122.394839 }, "costPerHour": 40.0, "costPerKilometer": 10.0, "loadLimits": { "weightKg": { "maxLoad": "150", "softMaxLoad": "100", "costPerUnitAboveSoftMax": 5.0 } } } ] } }
Example response
The response now contains two routes, one for each vehicle. The optimizer determines that using both vehicles is more cost-effective than using one vehicle and incurring the soft limit penalty.
See a response to the request with a soft load limit
{ "routes": [ { "vehicleStartTime": "2023-01-13T16:00:00Z", "vehicleEndTime": "2023-01-13T16:13:31Z", "visits": [ { "startTime": "2023-01-13T16:03:53Z", "detour": "0s", "loadDemands": { "weightKg": { "amount": "-50" } } } ], "transitions": [ { "travelDuration": "233s", "travelDistanceMeters": 794, "waitDuration": "0s", "totalDuration": "233s", "startTime": "2023-01-13T16:00:00Z", "vehicleLoads": { "weightKg": { "amount": "50" } } }, { "travelDuration": "328s", "travelDistanceMeters": 1188, "waitDuration": "0s", "totalDuration": "328s", "startTime": "2023-01-13T16:08:03Z", "vehicleLoads": { "weightKg": {} } } ], "metrics": { "performedShipmentCount": 1, "travelDuration": "561s", "visitDuration": "250s", "totalDuration": "811s", "travelDistanceMeters": 1982, "maxLoads": { "weightKg": { "amount": "50" } } }, "routeCosts": { "model.vehicles.cost_per_kilometer": 19.82, "model.vehicles.cost_per_hour": 9.01 }, "routeTotalCost": 28.83 }, { "vehicleIndex": 1, "vehicleStartTime": "2023-01-13T16:00:00Z", "vehicleEndTime": "2023-01-13T16:21:43Z", "visits": [ { "shipmentIndex": 1, "startTime": "2023-01-13T16:05:54Z", "detour": "0s", "loadDemands": { "weightKg": { "amount": "-60" } } }, { "shipmentIndex": 2, "startTime": "2023-01-13T16:13:52Z", "detour": "473s", "loadDemands": { "weightKg": { "amount": "-30" } } } ], "transitions": [ { "travelDuration": "354s", "travelDistanceMeters": 1196, "waitDuration": "0s", "totalDuration": "354s", "startTime": "2023-01-13T16:00:00Z", "vehicleLoads": { "weightKg": { "amount": "90" } } }, { "travelDuration": "228s", "travelDistanceMeters": 808, "waitDuration": "0s", "totalDuration": "228s", "startTime": "2023-01-13T16:10:04Z", "vehicleLoads": { "weightKg": { "amount": "30" } } }, { "travelDuration": "221s", "travelDistanceMeters": 655, "waitDuration": "0s", "totalDuration": "221s", "startTime": "2023-01-13T16:18:02Z", "vehicleLoads": { "weightKg": {} } } ], "metrics": { "performedShipmentCount": 2, "travelDuration": "803s", "visitDuration": "500s", "totalDuration": "1303s", "travelDistanceMeters": 2659, "maxLoads": { "weightKg": { "amount": "90" } } }, "routeCosts": { "model.vehicles.cost_per_kilometer": 26.59, "model.vehicles.cost_per_hour": 14.48 }, "routeTotalCost": 41.07 } ], "metrics": { "aggregatedRouteMetrics": { "performedShipmentCount": 3, "travelDuration": "1364s", "visitDuration": "750s", "totalDuration": "2114s", "travelDistanceMeters": 4641, "maxLoads": { "weightKg": { "amount": "90" } } }, "usedVehicleCount": 2, "earliestVehicleStartTime": "2023-01-13T16:00:00Z", "latestVehicleEndTime": "2023-01-13T16:21:43Z", "totalCost": 69.90, "costs": { "model.vehicles.cost_per_kilometer": 46.41, "model.vehicles.cost_per_hour": 23.49 } } }
The following fields show how the optimizer split the shipments across the
two vehicles to keep the loads under its 100 weightKg soft limit.
- The first route (
vehicleIndex: 0) handles the 50weightKgshipment. ItsmaxLoadsis "50", which is below the soft limit. - The second route (
vehicleIndex: 1) handles the 60 and 30weightKgshipments. ItsmaxLoadsis "90", which is also below the soft limit. - Because neither vehicle violates its soft limit, the
routeCostsfor both routes show nocostPerUnitAboveSoftMaxpenalty.