EP-4736095-A1 - MACHINE LEARNING TO REDUCE RESOURCES FOR GENERATING SOLUTIONS TO MULTI-NODE PROBLEMS

Abstract

In an embodiment, a method may include accessing, by a computing system, a multi-node problem. The multi-node problem may include a plurality of nodes, each respective node having one or more node features. The method may include providing, by the computing system, each respective node with each respective node feature to a machine learning model. The method may include determining, by the computing system using the machine learning model, a subset of nodes of the plurality of nodes based at least in part on the respective node features. The method may include calculating, by the computing system, one or more solutions to the multi-node problem based at least in part on the subset of nodes. The method may include storing, by the computing system, the one or more solutions to the multi-node problem in a computer memory.

Inventors

• OROOJLOOYJADID, AFSHIN
• Rezaeian, Amir Hossein

Assignees

• Oracle International Corporation

Dates

Publication Date

20260506

Application Date

20240626

Claims (20)

1. 1. A method comprising: accessing, by a computing system, a multi-node problem comprising a plurality of nodes, each respective node having one or more node features; providing, by the computing system, each respective node with each respective node feature to a machine learning model; determining, by the computing system using the machine learning model, a subset of nodes of the plurality of nodes based at least in part on the respective node features; and calculating, by the computing system, one or more solutions to the multi-node problem based at least in part on the subset of nodes; storing, by the computing system, the one or more solutions to the multi-node problem in a computer memory.
2. 2. The method of claim 1, wherein the subset of nodes of the plurality of nodes comprises non-zero nodes.
3. 3. The method of claim 1 or 2, wherein providing each respective node and each respective node feature further comprises generating an embedded vector comprising one or more dimensions corresponding to each respective node feature.
4. 4. The method of any preceding claim, wherein determining the subset of nodes further comprises: determining, by the computing system using the machine learning model, a minimum value associated with each of the plurality of nodes; determining, by the computing system using the machine learning model, a probability 7 that the minimum value associated wi th each of the plurality' of nodes is a value associated with each node in an optimal solution; and identifying, by the computing system, the subset of nodes, each node of the subset of nodes identified as non-zero and characterized by a probability greater than or equal to a predetermined threshold.
5. 5. The method of any preceding claim, wherein the machine learning model comprises a graph neural network.
6. 6. The method of any preceding claim, wherein the multi-node problem represents a multi-echelon inventory' optimization problem.
7. 7. The method of any preceding claim, wherein calculating the one or more solutions to the multi-node problem utilizes a guaranteed service model.
8. 8. The method of any preceding claim, wherein the machine learning model is trained at least in part on a historical dataset comprising a plurality' of solutions to multi-node problems.
9. 9. The method of any preceding claim, wherein the one or more solutions to the multi-node problem are provided to a second computing system.
10. 10. A computing system comprising: one or more processors; and a non-transitory computer readable medium comprising instructions that, when executed by the one or more processors, cause the system to perform operations to: access, by the computing system, a multi-node problem comprising a plurality of nodes, each respective node having one or more node features; provide, by the computing system, each respective node with each respective node feature to a machine learning model; determine, by the computing system using the machine learning model, a subset of nodes of the plurality of nodes based at least in part on the respective node features; and calculate, by the computing system, one or more solutions to the multinode problem based at least in part on the subset of nodes; store, by the computing system, the one or more solutions to the multinode problem in a computer memory'.
11. 11. The system of claim 10, wherein the machine learning model includes an embedding module.
12. 12. The system of claim 10 or 11, wherein the subset of nodes of the plurality of nodes comprises non-zero nodes.
13. 13. The system of any of claims 10 to 12, wherein the multi-node problem represents a multi-echelon inventory optimization problem.
14. 14. The system of any of claims 10 to 13, wherein calculating the one or more solutions to the multi-node problem comprises utilizing a guaranteed service model.
15. 15. The system of any of claims 10 to 14, wherein the historical dataset comprises a plurality of solutions to multi-node problems.
16. 16. A non-transitory computer-readable storage medium storing a set of instructions that, when executed by one or more processors of a computer system, cause the computer system to perform operations comprising: accessing, by a computing system, a multi-node problem comprising a plurality of nodes, each respective node having one or more node features; providing, by the computing system, each respective node with each respective node feature to a machine learning model; determining, by the computing system using the machine learning model, a subset of nodes of the plurality of nodes based at least in part on the respective node features; and calculating, by the computing system, one or more solutions to the multi-node problem based at least in part on the subset of nodes; storing, by the computing system, the one or more solutions to the multi-node problem in a computer memory.
17. 17. The non-transitory computer-readable storage medium of claim 16, wherein the subset of nodes of the plurality of nodes comprises non-zero nodes.
18. 18. The non-transitory computer-readable storage medium of claim 16 or 17, wherein determining the subset of nodes further comprises: determining, by the computing system using the machine learning model, a minimum value associated with each of the plurality of nodes; determining, by the computing system using the machine learning model, a probability 7 that the minimum value associated with each of the plurality' of nodes is a value associated with each node in an optimal solution; and identifying, by the computing system, the subset of nodes, each node of the subset of nodes identified as non-zero and characterized by a probability greater than or equal to a predetermined threshold.
19. 19. The non-transi lory computer-readable storage medium of any of claims 16 to 18, wherein calculating the one or more solutions to the multi-node problem and calculating the one or more updated solutions comprises utilizing a guaranteed service model.
20. 20. The non-transitory computer-readable storage medium of any of claims 16 to 19, wherein the machine learning model includes an embedding module.

Description

MACHINE LEARNING TO REDUCE RESOURCES FOR GENERATING SOLUTIONS TO MULTI-NODE PROBLEMS CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application claims the benefit and priority of U.S. Patent Application No. 18/343,292, filed June 28, 2023, entitled “MACHINE LEARNING TO REDUCE RESOURCES FOR GENERATING SOLUTIONS TO MULTI-NODE PROBLEMS”, which is hereby incorporated by reference in its entirety. TECHNICAL FIELD [0002] This disclosure is in the technical field of computing systems and relates to increasing the efficiency of computing systems in generating solutions to multi-node problems. BACKGROUND [0003] This disclosure relates generally to increased efficiency in performing resourceintensive computations. More specifically, this disclosure relates to generating solutions to multi-node problems. BRIEF SUMMARY [0004] In an embodiment, a method may include accessing, by a computing system, a multi-node problem. The multi-node problem may include a plurality of nodes, each respective node having one or more node features. The method may include providing, by the computing system, each respective node with each respective node feature to a machine learning model. The method may include determining, by the computing system using the machine learning model, a subset of nodes of the plurality of nodes based at least in part on the respective node features. The method may include calculating, by the computing system, one or more solutions to the multi-node problem based at least in part on the subset of nodes. The method may include storing, by the computing system, the one or more solutions to the multi-node problem in a computer memory. [0005] In some embodiments, the subset of nodes of the plurality of nodes may include non-zero nodes. In some embodiments, providing each respective node and each respective node feature may further include generating an embedded vector may include one or more dimensions corresponding to each respective node feature. [0006] In some embodiments, determining the subset of nodes further may include determining, by the computing system using the machine learning model, a minimum value associated with each of the plurality of nodes. Determining the subset may further include determining, by the computing system using the machine learning model, a probability that the minimum value associated with each of the plurality of nodes is a value associated with each node in an optimal solution. Determining the subset may also include identifying, by the computing system, the subset of nodes, each node of the subset of nodes identified as nonzero and characterized by a probability greater than or equal to a predetermined threshold. [0007] In some embodiments, the machine learning model may include a graph neural network. The multi-node problem may represent a multi-echelon inventory optimization problem. In some embodiments, the computer system may utilize a guaranteed service model to calculate the one or more solutions to the multi-node problem. The machine learning model may be trained at least in part on a historical dataset may include a plurality of solutions to multi-node problems. In some embodiments, the one or more solutions to the multi-node problem may be provided to a second computing system. [0008] In an embodiment, a computing system may include one or more processors. The computing system may also include a non-transitory computer readable medium may include instructions that, when executed by the one or more processors, cause the computing system to perform operations. The operations may cause the computing system to access a multinode problem. The multi-node problem may include a plurality of nodes, each respective node having one or more node features. The computing system may then provide each respective node with each respective node feature to a machine learning model. The computing system may then determine, using the machine learning model, a subset of nodes of the plurality of nodes based at least in part on the respective node features. The computing system may then calculate one or more solutions to the multi-node problem based at least in part on the subset of nodes. The computing system may store the one or more solutions to the multi-node problem in a computer memory. [0009] In some embodiments, the machine learning model includes an embedding module. The subset of nodes of the plurality of nodes may include non-zero nodes. The multi-node problem may represent a multi -echelon inventory optimization problem. The computing system may utilize a guaranteed service model to calculating the one or more solutions to the multi-node problem. In some embodiments, the historical dataset may include a plurality of solutions to multi-node problems. [0010] In an embodiment, a non-transitory computer-readable storage medium may store a set of instructions. The instructions, when executed by one or more processors of a computing system, may cause the computing system to pe