PRODUCTION OPTIMIZER FOR SUPPLY CHAIN MANAGEMENT

• US 20060241986A1
• Filed: 04/12/2006
• Published: 10/26/2006
• Est. Priority Date: 04/22/2005
• Status: Active Grant

• Current Assignee: Charles N. Harper
• Original Assignee: Charles N. Harper
• Inventors: Harper, Charles N.
• Granted Patent: US 8,112,300 B2
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/06316 Sequencing of tasks or work
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: Y02P 90/80 Management or planning

Abstract

One embodiment of the invention provides a method for optimizing a supply chain management (SCM) problem. A genetic algorithm optimization technique may be used to generate a production solution for the production side of an SCM problem and an ant colony optimization technique may be used to generate a solution for the distribution side of the SCM problem. Together, the genetic algorithm optimization technique and the ant colony optimization technique operate to quickly identify high-quality solutions to an SCM problem.

Claims

1. A method of optimizing a supply chain, comprising:

a) generating an optimized production solution for the supply chain by using a genetic algorithm optimization technique to optimize a population of seed solutions; and

b) generating an optimized distribution solution for the optimized production solution using an ant colony optimization technique.

2. The method of claim 1, wherein generating an optimized production solution for the supply chain, comprises:

a) reading input data, wherein the input data describes a supply chain problem to be optimized;

b) generating the population of seed solutions, wherein each solution in the population specifies a feasible solution for a supply chain problem to be optimized;

c) evaluating each solution in the population and sorting the solutions in the population according to the evaluation; and

d) performing the genetic algorithm optimization technique on the population until a termination condition specified by the input data is satisfied.

3. The method of claim 2, wherein performing the genetic algorithm optimization technique comprises:

a) modifying at least one aspect of at least one of the solutions in the population;

c) evaluating the performance of the modified solution;

d) adding the modified solution to the population; and

e) removing a solution from the population with the poorest evaluation, relative to other solutions in the population.

4. The method of claim 3, wherein modifying at least one aspect of at least one of the solution in the population, comprises:

a) selecting a solution modification technique;

b) selecting the at least one solution from the population to modify; and

c) modifying the at least one solution, according to the selected solution modification technique.

5. The method of claim 3, wherein the solution modification technique randomly modifies at least one aspect of the at least one solution.

6. The method of claim 3, wherein the solution modification technique modifies the at least one solution according to a heuristic rule.

7. The method of claim 3, wherein the solution modification technique cross-breeds two or more solutions to generate the modified solution.

8. The method of claim 3, wherein modifying at least one aspect of at least one of the solutions in the population comprises probabilistically selecting one or more solutions, wherein a solution with a superior evaluation is more likely to be selected for modification than a solution with an inferior evaluation.

9. The method of claim 1, wherein each production solution specifies at least a production level for each plant, for each day of an optimization period.

10. The method of claim 1, wherein the population of seed solutions includes a first group of solutions and a second group of solutions, wherein the first group of solutions are generated according to heuristic rules and wherein the second group of solutions are randomly generated.

11. The method of claim 1, wherein generating the optimized distribution solution for the optimized production solution using the ant colony optimization technique, comprises:

a) initializing a plant weight vector for each production plant in the supply chain;

b) initializing an installation vector for each delivery location in the supply chain; and

c) generating the distribution solution based on a set of weights specified by components of the plant weight vectors and installation vectors, wherein the distribution solution describes one or more delivery routes from a production plant to at least one delivery location.

12. The method of claim 11, wherein components of the plant weight vector indicate preferences for a delivery from the plant represented by the plant weight vector to an installation represented by a vector component.

13. The method of claim 11, wherein components of installation vector indicate a preference for a subsequent delivery from the installation represented by the installation vector to an installation represented by a vector component.

14. A computer-readable medium containing a program which, when executed, performs operations for optimizing a supply chain, comprising:

a) generating an optimized production solution for the supply chain by using a genetic algorithm optimization technique to optimize a population of seed solutions; and

b) generating an optimized distribution solution for the optimized production solution using an ant colony optimization technique.

15. The computer-readable medium of claim 14, wherein the operation for generating an optimized production solution for the supply chain comprise:

a) reading input data, wherein the input data describes a supply chain problem to be optimized;

b) generating the population of seed solutions, wherein each solution in the population specifies a feasible solution for a supply chain problem to be optimized;

c) evaluating each solution in the population and sorting the solutions in the population according to the evaluation; and

d) performing the genetic algorithm optimization technique on the population until a termination condition specified by the input data is satisfied.

16. The computer-readable medium of claim 15, wherein the operations for performing the genetic algorithm optimization technique, comprise:

a) modifying at least one aspect of at least one of the solutions in the population;

c) evaluating the performance of the modified solution;

d) adding the modified solution to the population; and

e) removing a solution from the population with the poorest evaluation, relative to other solutions in the population.

17. The computer-readable medium of claim 16, wherein the operations for modifying at least one aspect of at least one of the solution in the population, comprise:

a) selecting a solution modification technique;

b) selecting the at least one solution from the population to modify; and

c) modifying the at least one solution, according to the selected solution modification technique.

18. The computer-readable medium of claim 16, wherein the solution modification technique randomly modifies at least one aspect of the at least one solution.

19. The computer-readable medium of claim 16, wherein the solution modification technique modifies the at least one solution according to a heuristic rule.

20. The computer-readable medium of claim 16, wherein the solution modification technique cross-breeds two or more solutions to generate the modified solution.

21. The computer-readable medium of claim 16, wherein modifying at least one aspect of at least one of the solutions in the population comprises probabilistically selecting one or more solutions, wherein a solution with a superior evaluation is more likely to be selected for modification than a solution with an inferior evaluation.

22. The computer-readable medium of claim 14, wherein each production solution specifies at least a production level for each plant, for each day of an optimization period.

23. The computer-readable medium of claim 14, wherein the population of seed solutions includes a first group of solutions and a second group of solutions, wherein the first group of solutions are generated according to heuristic rules and wherein the second group of solutions are randomly generated.

24. The computer-readable medium of claim 14, wherein the operations for generating the optimized distribution solution for the optimized production solution using the ant colony optimization technique, comprise:

a) initializing a plant weight vector for each production plant in the supply chain;

b) initializing an installation vector for each delivery location in the supply chain; and

c) generating the distribution solution based on a set of weights specified by components of the plant weight vectors and installation vectors, wherein the distribution solution describes one or more delivery routes from a production plant to at least one delivery location.

25. The computer-readable medium of claim 24, wherein components of the plant weight vector indicate preferences for a delivery from the plant represented by the plant weight vector to an installation represented by a vector component.

26. The computer-readable medium of claim 24, wherein components of installation vector indicate a preference for a subsequent delivery from the installation represented by the installation vector to an installation represented by a vector component.

27. A computing device used to optimize operations of a supply chain, comprising:

a) a processor;

b) a production optimizer which, when executed by the processor, is configured to generate an optimized production solution for the supply chain by using a genetic algorithm optimization technique to optimize a population of seed solutions; and

c) a distribution optimizer which, when executed by the processor, is configured to generate an optimized distribution solution for the optimized production solution using an ant colony optimization technique.

28. The computing device of claim 27, wherein the production optimizer is configured to generate an optimized production solution for the supply chain by performing the steps of:

a) reading input data, wherein the input data describes a supply chain problem to be optimized;

b) generating the population of seed solutions, wherein each solution in the population specifies a feasible solution for a supply chain problem to be optimized;

c) evaluating each solution in the population and sorting the solutions in the population according to the evaluation; and

d) performing the genetic algorithm optimization technique on the population until a termination condition specified by the input data is satisfied.

29. The computing device of claim 28, wherein the step of performing the genetic algorithm optimization technique comprises:

a) modifying at least one aspect of at least one of the solutions in the population;

c) evaluating the performance of the modified solution;

d) adding the modified solution to the population; and

e) removing a solution from the population with the poorest evaluation.

30. The computing device of claim 29, wherein the step of modifying at least one aspect of at least one of the solution in the population, comprises:

a) selecting a solution modification technique;

b) selecting the at least one solution from the population to modify; and

c) modifying the at least one solution, according to the selected solution modification technique.

31. The computing device of claim 29, wherein the solution modification technique randomly modifies at least one aspect of the at least one solution.

32. The computing device of claim 29, wherein the solution modification technique modifies the at least one solution according to a heuristic rule.

33. The computing device of claim 29, wherein the solution modification technique cross-breeds two or more solutions to generate the modified solution.

34. The computing device of claim 29, wherein the step of modifying at least one aspect of at least one of the solutions in the population comprises probabilistically selecting one or more solutions, wherein a solution with a superior evaluation is more likely to be selected for modification than a solution with an inferior evaluation.

35. The computing device of claim 27, wherein each production solution specifies at least a production level for each plant, for each day of an optimization period.

36. The computing device of claim 27, wherein the population of seed solutions includes a first group of solutions and a second group of solutions, wherein the first group of solutions are generated according to heuristic rules and wherein the second group of solutions are randomly generated.

37. The computing device of claim 27, wherein the distribution optimizer is configured to generate the optimized distribution solution for the optimized production solution by performing the steps of:

a) initializing a plant weight vector for each production plant in the supply chain;

b) initializing an installation vector for each delivery location in the supply chain; and

c) generating the distribution solution based on a set of weights specified by components of the plant weight vectors and installation vectors, wherein the distribution solution describes one or more delivery routes from a production plant to at least one delivery location.

38. The computing device of claim 37, wherein components of the plant weight vector indicate preferences for a delivery from the plant represented by the plant weight vector to an installation represented by a vector component.

39. The computing device of claim 37, wherein components of installation vector indicate a preference for a subsequent delivery from the installation represented by the installation vector to an installation represented by a vector component.