Optimizing pallet location in a warehouse

Patent metadata

Field	Value
Publication number	US-11989689-B2
Application number	US-202217877762-A
Country	US
Kind code	B2
Filing date	Jul 29, 2022
Priority date	Nov 19, 2019
Publication date	May 21, 2024
Grant date	May 21, 2024

Abstract

A computer-based technology is provided to optimize a warehouse space, such as warehouse racks. The technology determines a storage duration of a pallet in a warehouse, and further determines an optimal storage location for the pallet in the warehouse. For example, the technology can determine how long an inbound pallet will stay in a warehouse, and locate an optimal area of the warehouse to store the pallet. Such an optimal pallet storage area is selected to reduce labor costs in transporting the pallet in, within, and out of the warehouse and further optimize the management of multiple pallets in the warehouse as a whole. In addition, the technology can consider the size of the pallet in determining the optimal storage location in the warehouse.

First claim

What is claimed is:

1. A system for managing a plurality of pallets in a storage facility, the system comprising:
a plurality of storage racks having a plurality of rack openings; and
a computer system including one or more processors that are programmed to perform operations including:
determining a duration percentile of a target pallet in the storage facility;
determining, based at least in part on the duration percentile, an expected storage duration of the target pallet in the storage facility;
calculating optimization values for the plurality of rack openings based at least in part on the expected storage duration;
identifying, among the plurality of rack openings, a target rack opening as a storage location for the target pallet, the target rack opening having an optimization value that satisfies a preset requirement; and
transmitting information identifying the storage location to equipment for placement of the target pallet,
wherein each of the optimization values includes a duration match value for each of the plurality of rack openings, wherein the duration match value for a rack opening with respect to a pallet represents proximity in duration value between a pallet storage duration associated with the rack opening and the expected storage duration of the pallet.

2. The system of claim 1 , wherein the operations further include:
determining a height of the target pallet; and
calculating the optimization values for the plurality of rack openings based on the expected storage duration and the height of the target pallet.

3. The system of claim 1 , further comprising a database that is programmed to store pallet allocation data that associates pallet storage durations with the plurality of rack openings.

4. The system of claim 1 , wherein the expected storage duration of the target pallet is determined based at least in part on historical inventory data, the historical inventory data identifying pallets stored in the storage facility, times at which the pallets were stored, and durations in which the pallets were stored.

5. The system of claim 4 , wherein the pallets in the historical inventory data are a same type of pallet as the target pallet.

6. The system of claim 4 , wherein the pallets in the historical inventory data have a same customer as the target pallet.

7. The system of claim 4 , wherein the pallets in the historical inventory data have a different customer than the target pallet.

8. The system of claim 1 , wherein determining the expected storage duration of the target pallet further comprises predicting the expected storage duration using a machine learning algorithm.

9. The system of claim 8 , wherein the machine learning algorithm is a neural network comprising an input layer for receiving input data, at least one middle layer configured to process the input data, and an output layer configured to generate the expected storage duration.

10. The system of claim 9 , wherein the output layer is further configured to generate the duration percentile of the target pallet.

11. The system of claim 9 , wherein the input data includes at least one of a supplier of a pallet, a customer of the pallet, storage requirements of the pallet, delivery requirements of the pallet, seasonality of items on the pallet, timing of storage of the pallet, and timing of delivery of the pallet.

12. The system of claim 1 , wherein a duration percentile that is less than a threshold value indicates a shorter storage duration time than a duration percentile that exceeds the threshold value.

13. The system of claim 1 , wherein the operations further comprise determining pallet storage durations for the plurality of rack openings based on a distance between each of the plurality of rack openings and an entrance of the storage facility.

14. The system of claim 1 , wherein the plurality of rack openings are mapped to different pallet duration percentiles, wherein the duration percentile of the target pallet is identified as a first pallet duration percentile of the pallet duration percentiles.

15. The system of claim 1 , wherein the plurality of rack openings includes a first rack opening and a second rack opening being arranged farther from an entrance of the storage facility than the first rack opening, the first rack opening mapped to a first duration percentile, and the second rack opening mapped to a second duration percentile greater than the first duration percentile.

16. The system of claim 15 , wherein the plurality of rank openings includes a third rack opening being arranged between the first rack opening and the second rack opening, the third rack opening mapped to a second duration percentile greater than the first duration percentile and less than the second duration percentile.

17. A computer-implemented method for managing a plurality of pallets in a storage facility, the method comprising:
determining, using at least one computing device, a duration percentile of a target pallet in the storage facility;
determining, using the at least one computing device and based at least in part on the duration percentile, an expected storage duration of the target pallet in the storage facility;
calculating, using the at least one computing device, optimization values for the plurality of rack openings based at least in part on the expected storage duration;
identifying, using the at least one computing device and among the plurality of rack openings, a target rack opening as a storage location for the target pallet, the target rack opening having an optimization value that satisfies a preset requirement; and
transmitting, using the at least one computing device, information identifying the storage location to equipment for placement of the target pallet,
wherein each of the optimization values includes a duration match value for each of the plurality of rack openings, wherein the duration match value for a rack opening with respect to a pallet represents proximity in duration value between a pallet storage duration associated with the rack opening and the expected storage duration of the pallet.

18. The method of claim 17 , wherein determining, using the at least one computing device, the expected storage duration of the target pallet further comprises predicting the expected storage duration using a machine learning algorithm.

19. The method of claim 17 , wherein a duration percentile that is less than a threshold value indicates a shorter storage duration time than a duration percentile that exceeds the threshold value.

20. The method of claim 17 , wherein the operations further comprise determining pallet storage durations for the plurality of rack openings based on a distance between each of the plurality of rack openings and an entrance of the storage facility.

Assignees

• Lineage Logistics Llc

Inventors

• Wintz Daniel Thomas
• Li Michael Lingzhi
• Wolf Elliott Gerard
• Mawer Chloe
• Voegele Caitlin
• Fang Zhou Daisy
• Choudhury Maya Ileana
• Long Julia
• Thattai Sudarsan
• Rivera Jeffrey Alvarez

Classifications

• G06Q10/087 Primary

  Inventory or stock management, e.g. order filling, procurement or balancing against orders · CPC title

• B65G1/1371

  with data records · CPC title

• B65G1/1373

  for fulfilling orders in warehouses · CPC title

• G06N20/00

  Machine learning · CPC title

• G06Q10/08 Primary

  Logistics, e.g. warehousing, loading or distribution; Inventory or stock management · CPC title