INTELLIGENT SHIPMENT ANALYSIS AND ROUTING
One example method includes receiving information for a shipment, the shipment comprising one or more pieces, the information comprising information about a destination; receiving, from a remote dimensioning device, dimension information, the dimension information providing a length, a width, and a height for each of the one or more pieces; transporting, using one or more first transport vehicles, the shipment to a first shipper facility; allocating, using a first trained machine learning (“ML”) model and the dimension information, each piece of the shipment to one or more second transport vehicles and loading each piece of the shipment according to the allocating, wherein the first trained ML model is trained based on length, width, and height dimension information for one or more pieces; transporting, using the one or more second transport vehicles, the shipment to a second shipper facility; allocating, using a second trained machine learning (“ML”) model and using the dimension information, each piece of the shipment to one or more third transport vehicles and loading each piece of the shipment according to the allocating, wherein the second trained ML model is trained based on at least two of the length, width, or height dimension information for the one or more pieces; and transporting, using the one or more third transport vehicles, the shipment from the third shipper facility to the destination.
1 . A method comprising:
receiving information for a shipment, the shipment comprising one or more pieces, the information comprising information about a destination;
receiving, from a remote dimensioning device, dimension information, the dimension information providing a length, a width, and a height for each of the one or more pieces;
transporting, using one or more first transport vehicles, the shipment to a first shipper facility;
allocating, using a first trained machine learning (“ML”) model and the dimension information, each piece of the shipment to one or more second transport vehicles and loading each piece of the shipment according to the allocating, wherein the first trained ML model is trained based on length, width, and height dimension information for one or more pieces;
transporting, using the one or more second transport vehicles, the shipment to a second shipper facility;
allocating, using a second trained machine learning (“ML”) model and using the dimension information, each piece of the shipment to one or more third transport vehicles and loading each piece of the shipment according to the allocating, wherein the second trained ML model is trained based on at least two of the length, width, or height dimension information for the one or more pieces; and
transporting, using the one or more third transport vehicles, the shipment from the third shipper facility to the destination.
2 . The method of claim 1 , further comprising determining a position on the second transport vehicle based on the dimension information.
3 . The method of claim 2 , wherein the determining the position comprises selecting a location from a set of predetermined locations for the respective second transport vehicle.
4 . The method of claim 2 , wherein determining the position comprises determining a vertical level within the second transport vehicle.
5 . The method of claim 1 , further comprising updating at least one of the second or third trained ML models based on the dimension information.
6 . The method of claim 1 , further comprising determining a position on the third transport vehicle based on the dimension information.
7 . The method of claim 1 , further comprising predicting, using a third ML model, the dimension information based on the information for the shipment.
8 . A system comprising:
a communications interface;
a non-transitory computer-readable medium; and
one or more processors communicatively coupled to the communications interface and the non-transitory computer-readable medium, the one or more processors configured to execute processor-executable instructions stored in the non-transitory computer-readable medium to:
receive information for a shipment, the shipment comprising one or more pieces, the information comprising information about a destination;
receive, from a remote dimensioning device, dimension information, the dimension information providing a length, a width, and a height for each of the one or more pieces;
allocating, using a first trained machine learning (“ML”) model and the dimension information, each piece of the shipment to one or more first transport vehicles, wherein the first trained ML model is trained based on length, width, and height dimension information for one or more pieces; and
allocate, using a second trained machine learning (“ML”) model and using the dimension information, each piece of the shipment to one or more second transport vehicles, wherein the second trained ML model is trained based on at least two of the length, width, or height dimension information for the one or more pieces.
9 . The system of claim 8 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to determine a position on the first transport vehicle based on the dimension information.
10 . The system of claim 9 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to select a location from a set of predetermined locations for the respective first transport vehicle.
11 . The system of claim 9 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to determine a vertical level within the first transport vehicle.
12 . The system of claim 8 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to update at least one of the first or second trained ML models based on the dimension information.
13 . The system of claim 8 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to determine a position on the second transport vehicle based on the dimension information.
14 . The system of claim 8 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to predict, using a third ML model, the dimension information based on the information for the shipment.
15 . A non-transitory computer-readable medium comprising processor-executable instructions configured to cause one or more processors to:
receive information for a shipment, the shipment comprising one or more pieces, the information comprising information about a destination;
receive, from a remote dimensioning device, dimension information, the dimension information providing a length, a width, and a height for each of the one or more pieces;
allocating, using a first trained machine learning (“ML”) model and the dimension information, each piece of the shipment to one or more first transport vehicles, wherein the first trained ML model is trained based on length, width, and height dimension information for one or more pieces; and
allocate, using a second trained machine learning (“ML”) model and using the dimension information, each piece of the shipment to one or more second transport vehicles, wherein the second trained ML model is trained based on at least two of the length, width, or height dimension information for the one or more pieces.
16 . The non-transitory computer-readable medium of claim 15 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to determine a position on the second transport vehicle based on the dimension information.
17 . The non-transitory computer-readable medium of claim 16 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to select a location from a set of predetermined locations for the respective second transport vehicle.
18 . The non-transitory computer-readable medium of claim 16 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to determine a vertical level within the second transport vehicle.
19 . The non-transitory computer-readable medium of claim 15 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to update at least one of the second or third trained ML models based on the dimension information.
20 . The non-transitory computer-readable medium of claim 15 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to determine a position on the second transport vehicle based on the dimension information.
21 . The non-transitory computer-readable medium of claim 15 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to predicted, using a third ML model, the dimension information based on the information for the shipment.