Generalizing an Optimized Supplier Allocation Plan

• US 20110125470A1
• Filed: 02/04/2011
• Published: 05/26/2011
• Est. Priority Date: 03/01/2002
• Status: Active Grant

• Current Assignee: Blue Yonder Group, Inc. (Panasonic Holdings Corporation)
• Original Assignee: i2 Technologies US Incorporated (Panasonic Holdings Corporation)
• Inventors: Hayes, Gayle Lynne, Yang, Jian, Miller, Mark Richard, Ganesan, Vijay Krishnan
• Granted Patent: US 9,875,447 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

Generating an optimized supplier allocation plan includes identifying parts and suppliers associated with an allocation problem, where each supplier can supply at least one part. One or more objective functions are selected. Each objective function has part variables, and each part variable represents a quantity of a part to be procured from a supplier. At least one constraint constraining at least one part variable is received. The one or more objective functions are optimized with respect to the at least one constraint to yield a value for each part variable. A quantity of each part to be procured from at least one supplier is determined according to the values to generate the optimized supplier allocation plan.

Claims

1. A method of generating an optimized supplier allocation plan, comprising:

initiating, by a computer, an object model that describes an allocation problem;

generating, by the computer, a mathematical model from the object model, the mathematical model comprising multiple objective functions that represent multiple objectives to be optimized;

optimizing, by the computer, each of the multiple objective functions to determine a corresponding normalization factor;

generating, by the computer, a combined objective function using each of the multiple objective functions normalized by the corresponding normalization factor and weighted by a corresponding weighting factor;

receiving, by the computer, one or more constraints that restrict the optimization of the combined objective function;

optimizing, by the computer, the combined objective function with respect to the one or more constraints; and

solving, by the computer, the mathematical model to yield an optimized solution to the allocation problem based on the optimization of the combined objective function.

2. The method of claim 1, wherein each of the one or more objective functions comprises a plurality of part variables, each part variable representing a quantity of a part to be procured from a supplier.

3. The method of claim 2, wherein the one or more constraints constrain at least one part variable.

4. The method of claim 2, further comprising:

determining a quantity of each part to be procured from at least one supplier according to the optimized solution of the allocation problem.

5. The method of claim 1, further comprising:

determining if the combined objective function can be optimized for all of the one or more constraints; and

responsive to a determination that the combined objective function cannot be optimized for all of the one or more constraints, optimizing the combined objective function for a portion of the one or more constraints.

6. The method of claim 1, further comprising:

determining if the combined objective function can be optimized for all of the one or more constraints; and

responsive to a determination that the combined objective function can be optimized for all of the one or more constraints, optimizing the combined objective function.

7. A system for generating an optimized supplier allocation plan, the system comprising one or more computer systems configured to:

initiate an object model that describes an allocation problem;

generate a mathematical model from the object model, the mathematical model comprising multiple objective functions that represent multiple objectives to be optimized;

optimize each of the multiple objective functions to determine a corresponding normalization factor;

generate a combined objective function using each of the multiple objective functions normalized by the corresponding normalization factor and weighted by a corresponding weighting factor;

receive one or more constraints that restrict the optimization of the combined objective function;

optimize the combined objective function with respect to the one or more constraints; and

solve the mathematical model to yield an optimized solution to the allocation problem based on the optimization of the combined objective function.

8. The system of claim 7, wherein each of the one or more objective functions comprises a plurality of part variables, each part variable representing a quantity of a part to be procured from a supplier.

9. The system of claim 8, wherein the one or more constraints constrain at least one part variable.

10. The system of claim 8, further configured to:

determine a quantity of each part to be procured from at least one supplier according to the optimized solution of the allocation problem.

11. The system of claim 7, further configured to:

determine if the combined objective function can be optimized for all of the one or more constraints; and

responsive to a determination that the combined objective function cannot be optimized for all of the one or more constraints, optimize the combined objective function for a portion of the one or more constraints.

12. The system of claim 7, further configured to:

determine if the combined objective function can be optimized for all of the one or more constraints; and

responsive to a determination that the combined objective function can be optimized for all of the one or more constraints, optimize the combined objective function.

13. A non-transitory computer-readable media embodied with software for generating an optimized supplier allocation plan, the software when executed using one or more computers is configured to:

initiate an object model that describes an all cation problem;

generate a mathematical model from the object model, the mathematical model comprising multiple objective functions that represent multiple objectives to be optimized;

optimize each of the multiple objective functions to determine a corresponding normalization factor;

generate a combined objective function using each of the multiple objective functions normalized by the corresponding normalization factor and weighted by a corresponding weighting factor;

receive one or more constraints that restrict the optimization of the combined objective function;

optimize the combined objective function with respect to the one or more constraints; and

solve the mathematical model to yield an optimized solution to the allocation problem based on the optimization of the combined objective function.

14. The computer-readable media of claim 13, wherein each of the one or more objective functions comprises a plurality of part variables, each part variable representing a quantity of a part to be procured from a supplier.

15. The computer-readable media of claim 14, wherein the one or more constraints constrain at least one part variable.

16. The computer-readable media of claim 14, wherein the software is further configured to:

determine a quantity of each part to be procured from at least one supplier according to the optimized solution of the allocation problem.

17. The computer-readable media of claim 13, wherein the software is further configured to:

determine if the combined objective function can be optimized for all of the one or more constraints; and

responsive to a determination that the combined objective function cannot be optimized for all of the one or more constraints, optimize the combined objective function for a portion of the one or more constraints.

18. The computer-readable media of claim 13, wherein the software is further configured to:

determine if the combined objective function can be optimized for all of the one or more constraints; and

responsive to a determination that the combined objective function can be optimized for all of the one or more constraints, optimize the combined objective function.