Generating an optimized pricing plan

• US 20030110066A1
• Filed: 12/09/2002
• Published: 06/12/2003
• Est. Priority Date: 12/10/2001
• Status: Active Grant

• Current Assignee: Blue Yonder Group, Inc. (Panasonic Holdings Corporation)
• Original Assignee: i2 Technologies US Incorporated (Panasonic Holdings Corporation)
• Inventors: Walser, Joachim Paul, Oren, LaMott G.
• Granted Patent: US 8,494,887 B2
• Time in Patent Office: Days
• US Class Current: 705/7
• 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 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions

Abstract

Generating an optimized pricing plan includes accessing a hierarchy comprising a sequence of levels, where each level includes an objective function and a set of constraints associated with the objective function. A mathematical programming model representing a pricing plan problem is determined for an item group comprising items. The following is repeated for each level of the sequence of levels: selecting a level comprising an objective function and a set of constraints associated with the objective function, determining an optimized boundary for the objective function, and adding a constraint generated from the optimized boundary to the set of constraints of a next level. An objective function of a last level of the sequence is optimized subject to the set of constraints associated with the objective function to yield an optimized result. An optimized pricing plan is generated in accordance with the optimized result.

Claims

1. A method for generating an optimized pricing plan, comprising:

accessing a hierarchy comprising a sequence of levels, each level comprising an objective function and a set of constraints associated with the objective function;

determining a mathematical programming model representing a pricing plan problem for an item group comprising a plurality of items, the mathematical programming model comprising a set of initial constraints;

repeating for each level of the sequence of levels;

selecting a level comprising an objective function and a set of constraints associated with the objective function;

adding the set of constraints associated with the objective function to the set of initial constraints;

optimizing the mathematical programming model to yield an optimized boundary for the objective function; and

adding a constraint generated from the optimized boundary to the set of constraints of a next level;

optimizing an objective function of a last level of the sequence subject to the set of constraints associated with the objective function to yield an optimized result, the set of constraints comprising a constraint generated from the optimized boundary of a previous level; and

generating an optimized pricing plan in accordance with the optimized result, the optimized pricing plan associating a price with each item of the item group.

2. The method of claim 1, wherein:

the mathematical programming model comprises a non-linear programming model; and

optimizing the objective function of the last level comprises optimizing the objective function using a non-linear programming technique.

3. The method of claim 1, further comprising:

splitting a category of items into a plurality of item groups, each item group comprising a set of items related by one or more item constraints; and

selecting an item group for which to generate the optimized pricing plan.

4. The method of claim 1, further comprising balancing an objective function across a plurality of time intervals of a time period, the optimized pricing plan listing a price for each item of the item group for each time interval of the time period.

5. The method of claim 1, further comprising balancing an objective function across a plurality of locations, the optimized pricing plan listing a price for each item of the item group for each location.

6. The method of claim 1, wherein optimizing the mathematical programming model to yield an optimized boundary comprises:

expressing the objective function using one or more slack variables; and

optimizing the one or more slack variables to determine the optimized boundary.

7. The method of claim 1, further comprising:

accessing a rounding rule for rounding a price of an item for each item of the item group;

accessing a plurality of propagation rules, each propagation rule for determining a price of a first item according to a price of a second item, each propagation rule associated with an item of the item group; and

repeating the following for each item of the item group;

applying the rounding rule to the item of the item group; and

applying a propagation rule for determining the price of the item according to a price of another item, if the item is associated with a propagation rule.

8. The method of claim 1, further comprising:

determining a price change for each item in accordance with the optimized pricing plan;

assessing a cost associated with each price change; and

adjusting a price change in response to a cost associated with the price change.

9. The method of claim 1, further comprising:

determining a price change for each item in accordance with the optimized pricing plan; and

prioritizing the price changes by associating a greater price change with a higher priority and associating a smaller price change with a lower priority.

10. The method of claim 1, further comprising:

repeating the steps of accessing, determining, repeating, optimizing, and generating to generate a plurality of local optimized results;

determining a global optimized result from the local optimized results; and

generating the optimized pricing plan from the global optimized result.

11. The method of claim 1, further comprising:

accessing a plurality of demand models;

accessing demand data describing the item group;

evaluating the demand models in accordance with the demand data;

selecting a demand model of the evaluated demand models in response to the evaluation;

calculating a price elasticity using the selected demand model; and

generating a constraint of the hierarchy in accordance with the price elasticity.

12. A system for generating an optimized pricing plan, comprising:

a database operable to store a hierarchy comprising a sequence of levels, each level comprising an objective function, and a set of constraints associated with the objective function; and

a server system coupled to the database and operable to;

determine a mathematical programming model representing a pricing plan problem for an item group comprising a plurality of items, the mathematical programming model comprising a set of initial constraints;

repeat for each level of the sequence of levels;

selecting a level comprising an objective function and a set of constraints associated with the objective function;

adding the set of constraints associated with the objective function to the set of initial constraints;

optimizing the mathematical programming model to yield an optimized boundary for the objective function; and

adding a constraint generated from the optimized boundary to the set of constraints of a next level;

optimize an objective function of a last level of the sequence subject to the set of constraints associated with the objective function to yield an optimized result, the set of constraints comprising a constraint generated from the optimized boundary of a previous level; and

generate an optimized pricing plan in accordance with the optimized result, the optimized pricing plan associating a price with each item of the item group.

13. The system of claim 12, wherein:

the mathematical programming model comprises a non-linear programming model; and

the server system is operable to optimize the objective function of the last level by optimizing the objective function using a non-linear programming technique.

14. The system of claim 12, wherein the server system is operable to:

split a category of items into a plurality of item groups, each item group comprising a set of items related by one or more item constraints; and

select an item group for which to generate the optimized pricing plan.

15. The system of claim 12, wherein the server system is operable to balance an objective function across a plurality of time intervals of a time period, the optimized pricing plan listing a price for each item of the item group for each time interval of the time period.

16. The system of claim 12, wherein the server system is operable to balance an objective function across a plurality of locations, the optimized pricing plan listing a price for each item of the item group for each location.

17. The system of claim 12, wherein the server system is operable to optimize the mathematical programming model to yield an optimized boundary by:

expressing the objective function using one or more slack variables; and

optimizing the one or more slack variables to determine the optimized boundary.

18. The system of claim 12, wherein the server system is operable to:

access a rounding rule for rounding a price of an item for each item of the item group;

access a plurality of propagation rules, each propagation rule for determining a price of a first item according to a price of a second item, each propagation rule associated with an item of the item group; and

repeat the following for each item of the item group;

applying the rounding rule to the item of the item group; and

applying a propagation rule for determining the price of the item according to a price of another item, if the item is associated with a propagation rule.

19. The system of claim 12, wherein the server system is operable to:

determine a price change for each item in accordance with the optimized pricing plan;

assess a cost associated with each price change; and

adjust a price change in response to a cost associated with the price change.

20. The system of claim 12, wherein the server system is operable to:

determine a price change for each item in accordance with the optimized pricing plan; and

prioritize the price changes by associating a greater price change with a higher priority and associating a smaller price change with a lower priority.

21. The system of claim 12, wherein the server system is operable to:

repeat the steps of accessing, determining, repeating, optimizing, and generating to generate a plurality of local optimized results;

determine a global optimized result from the local optimized results; and

generate the optimized pricing plan from the global optimized result.

22. The system of claim 12, wherein the server system is operable to:

access a plurality of demand models;

access demand data describing the item group;

evaluate the demand models in accordance with the demand data;

select a demand model of the evaluated demand models in response to the evaluation;

calculate a price elasticity using the selected demand model; and

generate a constraint of the hierarchy in accordance with the price elasticity.

23. Software for generating an optimized pricing plan, the software encoded in media and when executed operable to:

access a hierarchy comprising a sequence of levels, each level comprising an objective function and a set of constraints associated with the objective function;

determine a mathematical programming model representing a pricing plan problem for an item group comprising a plurality of items, the mathematical programming model comprising a set of initial constraints;

repeat for each level of the sequence of levels;

selecting a level comprising an objective function and a set of constraints associated with the objective function;

adding the set of constraints associated with the objective function to the set of initial constraints;

optimizing the mathematical programming model to yield an optimized boundary for the objective function; and

adding a constraint generated from the optimized boundary to the set of constraints of a next level;

optimize an objective function of a last level of the sequence subject to the set of constraints associated with the objective function to yield an optimized result, the set of constraints comprising a constraint generated from the optimized boundary of a previous level; and

generate an optimized pricing plan in accordance with the optimized result, the optimized pricing plan associating a price with each item of the item group.

24. The software of claim 23, wherein:

the mathematical programming model comprises a non-linear programming model; and

the software is operable to optimize the objective function of the last level by optimizing the objective function using a non-linear programming technique.

25. The software of claim 23, operable to:

split a category of items into a plurality of item groups, each item group comprising a set of items related by one or more item constraints; and

select an item group for which to generate the optimized pricing plan.

26. The software of claim 23, operable to balance an objective function across a plurality of time intervals of a time period, the optimized pricing plan listing a price for each item of the item group for each time interval of the time period.

27. The software of claim 23, operable to balance an objective function across a plurality of locations, the optimized pricing plan listing a price for each item of the item group for each location.

28. The software of claim 23, operable to optimize the mathematical programming model to yield an optimized boundary by:

expressing the objective function using one or more slack variables; and

optimizing the one or more slack variables to determine the optimized boundary.

29. The software of claim 23, operable to:

access a rounding rule for rounding a price of an item for each item of the item group;

access a plurality of propagation rules, each propagation rule for determining a price of a first item according to a price of a second item, each propagation rule associated with an item of the item group; and

repeat the following for each item of the item group;

applying the rounding rule to the item of the item group; and

applying a propagation rule for determining the price of the item according to a price of another item, if the item is associated with a propagation rule.

30. The software of claim 23, operable to:

determine a price change for each item in accordance with the optimized pricing plan;

assess a cost associated with each price change; and

adjust a price change in response to a cost associated with the price change.

31. The software of claim 23, operable to:

determine a price change for each item in accordance with the optimized pricing plan; and

prioritize the price changes by associating a greater price change with a higher priority and associating a smaller price change with a lower priority.

32. The software of claim 23, operable to:

repeat the steps of accessing, determining, repeating, optimizing, and generating to generate a plurality of local optimized results;

determine a global optimized result from the local optimized results; and

generate the optimized pricing plan from the global optimized result.

33. The software of claim 23, operable to:

access a plurality of demand models;

access demand data describing the item group;

evaluate the demand models in accordance with the demand data;

select a demand model of the evaluated demand models in response to the evaluation;

calculate a price elasticity using the selected demand model; and

generate a constraint of the hierarchy in accordance with the price elasticity.

34. A system for generating an optimized pricing plan, comprising:

means for accessing a hierarchy comprising a sequence of levels, each level comprising an objective function and a set of constraints associated with the objective function;

means for determining a mathematical programming model representing a pricing plan problem for an item group comprising a plurality of items;

means for repeating for each level of the sequence of levels;

selecting a level comprising an objective function and a set of constraints associated with the objective function;

determining an optimized boundary for the objective function; and

adding a constraint generated from the optimized boundary to the set of constraints of a next level;

means for optimizing an objective function of a last level of the sequence subject to the set of constraints associated with the objective function to yield an optimized result, the set of constraints comprising a constraint generated from the optimized boundary of a previous level; and

means for generating an optimized pricing plan in accordance with the optimized result, the optimized pricing plan associating a price with each item of the item group.

35. A method for generating an optimized pricing plan, comprising:

accessing a hierarchy comprising a sequence of levels, each level comprising an objective function and a set of constraints associated with the objective function;

splitting a category of items into a plurality of item groups, each item group comprising a set of items related by one or more item constraints;

selecting an item group for which to generate an optimized pricing plan;

determining a mathematical programming model representing a pricing plan problem for the selected item group, the mathematical programming model comprising a non-linear programming model;

repeating for each level of the sequence of levels;

selecting a level comprising an objective function and a set of constraints associated with the objective function;

determining an optimized boundary for the objective function by expressing the objective function using one or more slack variables, and optimizing the one or more slack variables to determine the optimized boundary; and

adding a constraint generated from the optimized boundary to the set of constraints of a next level;

optimizing an objective function of a last level of the sequence, using a non-linear programming technique, subject to the set of constraints associated with the objective function to yield an optimized result, the set of constraints comprising a constraint generated from the optimized boundary of a previous level;

accessing a rounding rule for rounding a price of an item for each item of the item group;

accessing a plurality of propagation rules, each propagation rule for determining a price of a first item according to a price of a second item, each propagation rule associated with an item of the item group;

repeating the following for each item of the item group;

applying the rounding rule to the item of the item group, and applying a propagation rule for determining the price of the item according to a price of another item, if the item is associated with a propagation rule; and

generating the optimized pricing plan in accordance with the optimized result, the optimized pricing plan associating a price with each item of the item group.