Systems and methods for vehicle routing

1. A system comprising:

one or more processors; and

one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising:

receiving one or more orders comprising a list of pallets;

grouping each pallet of the pallets into one or more groups using one or more respective qualities of each respective pallet;

for each group of the one or more groups, building stacks of pallets using at least three different stacking models;

selecting a final stack list of a model of the at least three different stacking models having a lowest number of stacks;

optimizing the final stack list using a probabilistic model;

using the final stack list, as optimized, to generate a loading plan, wherein the loading plan comprises respective locations where each of one or more stacks of the final stack list is to be loaded within a delivery vehicle; and

displaying, on an electronic device of a user, an augmented reality environment of an inside view of a respective delivery vehicle showing respective locations for loading the one or more stacks within the respective delivery vehicle to allow the user to view the display to execute the loading plan within the respective delivery vehicle.

2. The system of claim 1 , wherein the computing instructions, when executed on the one or more processors, further cause the one or more processors to perform operations comprising:

generating a routing plan comprising a sequence of stops, wherein each stop of the sequence of stops is associated with one or more respective stacks of the stacks based on at least the final stack list or the loading plan;

transmitting the routing plan to an electronic device of the respective delivery vehicle to deliver the one or more respective stacks associated with each stop, wherein the respective delivery vehicle comprises an autonomous vehicle; and

displaying the routing plan for delivery of the one or more stacks in the loading plan on the electronic device of the delivery vehicle.

3. The system of claim 1 , wherein a stacking model of the at least three different stacking models comprises a first fit algorithm.

4. The system of claim 3 , wherein the first fit algorithm comprises:

inserting a pallet into a first stack in a sequence of stacks having enough space to accommodate the pallet.

5. The system of claim 1 , wherein a stacking model of the at least three different stacking models comprises a best fit algorithm.

6. The system of claim 5 , wherein the best fit algorithm comprises:

inserting a pallet into a stack having a least amount of remaining space in the stack after insertion of the pallet.

7. The system of claim 1 , wherein a stacking model of the at least three different stacking models comprises a worst fit algorithm.

8. The system of claim 7 , wherein the worst fit algorithm comprises:

inserting a pallet into a stack having a most amount of remaining space in the stack after insertion of the pallet.

9. The system of claim 1 , wherein the probabilistic model comprises simulated annealing.

10. The system of claim 1 , wherein building the stacks of pallets using the at least three different stacking models comprises:

initializing a new stack when a pallet does not fit in any stack according to at least one of the at least three different stacking models.

11. A method implemented via execution of computing instructions configured to run at one or more processors and configured to be stored at non-transitory computer-readable media, the method comprising:

receiving one or more orders comprising a list of pallets;

grouping each pallet of the pallets into one or more groups using one or more respective qualities of each respective pallet;

for each group of the one or more groups, building stacks of pallets using at least three different stacking models;

selecting a final stack list of a model of the at least three different stacking models having a lowest number of stacks;

optimizing the final stack list using a probabilistic model;

using the final stack list, as optimized, to generate a loading plan, wherein the loading plan comprises respective locations where each of one or more stacks of the final stack list is to be loaded within a delivery vehicle; and

displaying, on an electronic device of a user, an augmented reality environment of an inside view of a respective delivery vehicle showing respective locations for loading the one or more stacks within the respective delivery vehicle to allow the user to view the display to execute the loading plan within the respective delivery vehicle.

12. The method of claim 11 further comprising:

generating a routing plan comprising a sequence of stops, wherein each stop of the sequence of stops is associated with one or more respective stacks of the stacks based on at least the final stack list or the loading plan;

transmitting the routing plan to an electronic device of the respective delivery vehicle to deliver the one or more respective stacks associated with each stop, wherein the respective delivery vehicle comprises an autonomous vehicle; and

displaying the routing plan for delivery of the one or more stacks in the loading plan on the electronic device of the delivery vehicle.

13. The method of claim 11 , wherein a stacking model of the at least three different stacking models comprises a first fit algorithm.

14. The method of claim 13 , wherein the first fit algorithm comprises:

inserting a pallet into a first stack in a sequence of stacks having enough space to accommodate the pallet.

15. The method of claim 11 , wherein a stacking model of the at least three different stacking models comprises a best fit algorithm.

16. The method of claim 15 , wherein the best fit algorithm comprises:

inserting a pallet into a stack having a least amount of remaining space in the stack after insertion of the pallet.

17. The method of claim 11 , wherein a stacking model of the at least three different stacking models comprises a worst fit algorithm.

18. The method of claim 17 , wherein the worst fit algorithm comprises:

inserting a pallet into a stack having a most amount of remaining space in the stack after insertion of the pallet.

19. The system of claim 1 , wherein the probabilistic model comprises simulated annealing.

20. The method of claim 11 , wherein building the stacks of pallets using the at least three different stacking models comprises:

initializing a new stack when a pallet does not fit in any stack according to at least one of the at least three different stacking models.