Title:

Kind
Code:

A1

Abstract:

An integrated decision support framework is disclosed, in which different types of decision-drivers from numerous sources can be converted into a unified decision network including, for example, both mathematical and node-edge graph representations. A graph-theoretic algorithm may be applied to the large problem (unified decision network) to detect and separate strongly-connected components. The strongly-connected components represent sub-problems that can be solved simultaneously. A dependency propagation technique may be used to properly order the sub-problems so they can be processed and solved sequentially and correctly. Each strongly-connected component (small sub-problem) can be delegated to a suitable decision generator depending on the types of relations included in the component. For example, a numerical solution algorithm may be used to solve the ordered, numerical relations sub-problems; an algebraic solution algorithm may be used to solve the ordered, geometric relations sub-problems; and a logical inference engine (algorithm) may be used to solve the ordered, logical relations sub-problems. Solutions thus derived can be propagated to the next stage of the decision resolution process until a complete problem is solved.

Inventors:

Chung, Jack C. H. (Cincinnati, OH, US)

Wang, Jia-yi (Mason, OH, US)

Wu, Chien-tai (Cincinnati, OH, US)

Wang, Jia-yi (Mason, OH, US)

Wu, Chien-tai (Cincinnati, OH, US)

Application Number:

10/055098

Publication Date:

07/24/2003

Filing Date:

01/22/2002

Export Citation:

Assignee:

Electronic Data Systems Corporation

Primary Class:

International Classes:

View Patent Images:

Related US Applications:

Primary Examiner:

BOYCE, ANDRE D

Attorney, Agent or Firm:

SIEMENS CORPORATION (Orlando, FL, US)

Claims:

1. A method for integrated decision support, comprising the steps of: receiving a plurality of decision inputs; converting a first plurality of said received decision inputs to a plurality of graph representations; converting a second plurality of said received decision inputs to a plurality of mathematical representations; decomposing said converted first plurality of said received decision inputs and said converted second plurality of said received decision inputs to a plurality of sub-problems; detecting a plurality of strongly-connected components associated with said plurality of sub-problems; and solving said plurality of sub-problems.

2. The method of claim 1, wherein the decomposing step further comprises the steps of: performing dependency propagation for said plurality of sub-problems; and placing said plurality of sub-problems in at least one predefined order for solution.

3. The method of claim 1, wherein the detecting step comprises executing a graph-theoretic algorithm for a plurality of mathematical equations associated with said plurality of strongly-connected components to prevent over-constraining.

4. The method of claim 1, wherein the decomposing step comprises decomposing said converted first plurality of said received decision inputs and said converted second plurality of said received decision inputs to a plurality of mathematical equations and algebraically solvable graph components.

5. The method of claim 1, wherein the detecting step comprises detecting a plurality of coupled relations.

6. The method of claim 1, wherein the detecting step comprises identifying a plurality of simultaneous equations.

7. The method of claim 1, wherein the solving step comprises solving a plurality of numerical sub-problems and a plurality of algebraic sub-problems.

8. The method of claim 1, wherein said solving step comprises: solving a plurality of numerical relations subproblems with a numerical solution algorithm; solving a plurality of geometric relations subproblems with an algebraic solution algorithm; and solving a plurality of logical relations subproblems with a logical inference solution algorithm.

9. The method of claim 1, wherein said plurality of decision inputs comprises at least one of: a plurality of option selection parameters; a plurality of equality relation parameters; a plurality of dependency parameters; a plurality of production rule parameters; a plurality of logical relation parameters; a plurality of inequality expression parameters; and a plurality of geometric constraint parameters.

10. The method of claim 1, wherein the solving step comprises solving a plurality of simultaneous equations with a Newton-Raphson algorithm or Modified Gram-Schmidt algorithm.

11. Software for integrated decision support, the software being embodied in a computer-readable medium and when executed operable to: receive a plurality of decision inputs; convert a first plurality of said received decision inputs to a plurality of graph representations; convert a second plurality of said received decision inputs to a plurality of mathematical representations; decompose said converted first plurality of said received decision inputs and said converted second plurality of said received decision inputs to a plurality of sub-problems; detect a plurality of strongly-connected components associated with said plurality of sub-problems; and solve said plurality of sub-problems.

12. A computer-implemented system for integrated decision support, comprising: a processor; and a data storage device coupled to said processor, said processor operable to: receive a plurality of decision inputs; convert a first plurality of said received decision inputs to a plurality of graph representations; convert a second plurality of said received decision inputs to a plurality of mathematical representations; decompose said converted first plurality of said received decision inputs and said converted second plurality of said received decision inputs to a plurality of sub-problems; detect a plurality of strongly-connected components associated with said plurality of sub-problems; and solve said plurality of sub-problems.

13. The system of claim 12, wherein said processor is further operable to: perform dependency propagation for said plurality of sub-problems; and place said plurality of sub-problems in at least one predefined order for solution.

14. The system of claim 12, wherein said processor is further operable to execute a graph-theoretic algorithm for a plurality of mathematical equations associated with said plurality of strongly-connected components to prevent over-constraining.

15. The system of claim 12, wherein said processor is further operable to decompose said converted first plurality of said received decision inputs and said converted second plurality of said received decision inputs to a plurality of mathematical equations and algebraically solvable graph components.

16. The system of claim 12, wherein said processor is further operable to detect a plurality of coupled relations.

17. The system of claim 12, wherein said processor is further operable to identify a plurality of simultaneous equations.

18. The system of claim 12, wherein said processor is further operable to solve a plurality of numerical sub-problems and a plurality of algebraic sub-problems.

19. The system of claim 12, wherein said processor is further operable to: solve a plurality of numerical relations sub-problems with a numerical solution algorithm; solve a plurality of geometric relations sub-problems with an algebraic solution algorithm; and solve a plurality of logical relations sub-problems with a logical inference solution algorithm.

20. The system of claim 12, wherein said plurality of decision inputs comprises at least one of: a plurality of option selection parameters; a plurality of equality relation parameters; a plurality of dependency parameters; a plurality of production rule parameters; a plurality of logical relation parameters; a plurality of inequality expression parameters; and a plurality of geometric constraint parameters.

21. The system of claim 12, wherein said processor is further operable to solve a plurality of simultaneous equations with a Newton-Raphson algorithm or Modified Gram-Schmidt algorithm.

22. A system for integrated decision support, comprising: means for receiving a plurality of decision inputs; means for converting a first plurality of said received decision inputs to a plurality of graph representations; means for converting a second plurality of said received decision inputs to a plurality of mathematical representations; means for decomposing said converted first plurality of said received decision inputs and said converted second plurality of said received decision inputs to a plurality of sub-problems; means for detecting a plurality of strongly-connected components associated with said plurality of sub-problems; and means for solving said plurality of sub-problems.

Description:

[0001] The present invention relates in general to product development management and, in particular, but not exclusively, to an integrated decision support framework for collaborative product development.

[0002] Collaborative product development is a product development process in which the entire product development team (e.g., designers, engineers, customers, suppliers, business partners, etc.) can readily exchange information about the product throughout its entire life cycle. The collaborative product development process can encompass requirements definition, design and analysis, manufacturing planning, product data management, and collaborative commerce (enhanced business-to-business interaction and cooperation) for the product involved. Taking advantage of the enhanced flexibility and increased resources provided through collaborative product development processes, business entities can develop higher quality products than their competitors and deliver them to market faster.

[0003] Fundamental to quality improvement and increased productivity of collaborative product development processes is the need for an effective decision-making and propagation mechanism (vehicle for making and carrying out decisions). In collaborative product development, the inputs to and drivers of the decision-making part of the process (so-called “decision-drivers”) are numerous and diverse. For example, automotive industry decision-drivers can include, but are not limited to, option selections (e.g., V8 versus V6 engine), equality relations (e.g., hole size=pin size+5 mm), dependencies (e.g., assemble part A before part B), production rules (e.g., if engine=V8, add fan), logical relations (e.g., product must contain either part A or parts B and C), conditional relations (e.g., if loading capacity>X, use roller bearing; or else, use ball bearing), inequality expressions (e.g., total cost<1000 dollars), and geometric constraints (e.g., two rows of seats must be 30 inches apart). In collaborative product development, such decision-drivers are inputs to the decision-making process that originate from numerous, diverse sources.

[0004] A significant problem with supporting the numerous, diverse types of decision-drivers in collaborative product development is that the different input drivers interact with each other and thus each one cannot be independently resolved. As such, there is no existing technique that can integrate these diverse input drivers and obtain an optimum solution in a way that violates no imposed constraints. Furthermore, in a practical industrial environment, a relatively large set of these input drivers (e.g., thousands) typically have to be addressed all at the same time. Consequently, a significant challenge in collaborative product development is to be able to formulate optimal decisions efficiently and resolve all conflicts in a reasonable way.

[0005] According to the present invention, disadvantages and problems associated with previous collaborative product development techniques have been substantially reduced or eliminated.

[0006] In one example embodiment of the present invention, an integrated decision support framework is provided, whereby among other things, mathematical constraints, logical relations, production rules, and dependencies in product development management systems can be integrated in the same framework to support new applications in variant product data management. In accordance with one example embodiment of the present invention, different types of decision-drivers from numerous sources can be converted into a unified decision network including, for example, both mathematical and node-edge graph representations. A graph-theoretic algorithm may be applied to the large problem (unified decision network) to detect and separate strongly-connected components. The strongly-connected components represent sub-problems that must be solved simultaneously. A dependency propagation technique may be used to properly order the sub-problems so they can be processed and solved sequentially and correctly. Each strongly-connected component (small sub-problem) can be delegated to a suitable decision generator depending on the types of relations included in the component. For example, a numerical solution algorithm may be used to solve the ordered, numerical relations sub-problems, an algebraic solution algorithm may be used to solve the ordered, geometric relations sub-problems, and a logical inference engine (algorithm) may be used to solve the ordered, logical relations sub-problems. Solutions thus derived can be propagated to the next stage of the decision resolution process until a complete problem is solved.

[0007] Particular embodiments of the present invention may provide one or more technical advantages. For example, certain embodiments of the present invention may provide an integrated decision support framework that can support diverse types of decision drivers to produce efficient decision-making and generate effective decision rationale. Certain embodiments of the present invention allow the diverse requirements generated throughout the product development cycle to be captured, managed, solved and propagated in a unified framework. As a result, users are freed from managing the complexity aspects of the problem and assuring the correctness of the solution. Instead, users are allowed to concentrate on their innovative activities and thus improve the overall productivity and quality of the product development process. The present invention's inherent capability of decomposing a large problem into a smaller sub-problems and organizing them into proper sequential order makes effective decision support of large industrial problems practical. As such, common tasks in product life cycle management, such as design configuration support, variant and diversity product structure, requirements-driven design, change and dependency management and propagation, and constraint-based design are all covered within the scope of the present invention. Also, the integrated decision support framework of the present invention can facilitate explanations of the decisions made, by providing decision rationale to illustrate how a particular decision is derived, what conflicts exist, and how to resolve them. This advantageous property is crucial in supporting human decision-making when trade-offs need to be made.

[0008] Other technical advantages of the present invention will be readily apparent to one skilled in the art from the following figures, description and claims.

[0009] For a more complete understanding of the present invention and its advantages, reference is now made to the following descriptions, taken in conjunction with the accompanying drawings, in which:

[0010]

[0011]

[0012]

[0013]

[0014] The preferred embodiment of the present invention and its advantages are best understood by referring to FIGS.

[0015] Essentially, in accordance with one example embodiment of the present invention, an integrated decision support framework is provided, whereby different types of decision-drivers from numerous sources can be converted into a unified decision network including, for example, both mathematical and node-edge graph representations. A graph-theoretic algorithm may be applied to the large problem (unified decision network) to detect and separate strongly-connected components. The strongly-connected components represent sub-problems that must be solved simultaneously. A dependency propagation technique may be used to properly order the sub-problems so they can be processed and solved sequentially and correctly. Each strongly-connected component (small sub-problem) can be delegated to a suitable decision generator depending on the types of relations included in the component. For example, a numerical solution algorithm may be used to solve the ordered, numerical relations sub-problems, an algebraic solution algorithm may be used to solve the ordered, geometric relations sub-problems, and a logical inference engine (algorithm) may be used to solve the ordered, logical relations sub-problems. Solutions thus derived can be propagated to the next stage of the decision resolution process until a complete problem is solved.

[0016]

[0017] For example, a “thin” (limited resources available) client

[0018] Notably, decision-driver information or decision information may be communicated from a thin client

[0019]

[0020]

[0021] At step

[0022] At step

[0023] Notably, although not a limitation on the scope of the invention, from a technical standpoint if using a numerical approach to solve the smaller groups of equations, it is important to consider performance of a singularity check of the numerical solutions in order to enhance solution accuracy and performance. In that regard, constraints that cause global singularity should be rejected. Also, the numerical solutions should be validated against physically infeasible situations such as, for example, a negative radius.

[0024] In order to decompose the graph representations of the large problem into a group of smaller sub-problems, the structure of the node-edge graph may be analyzed. An analysis of the graph's structure shows (e.g., see graph

[0025] At step

[0026] At step

[0027] At step

[0028] At step

[0029] Although a preferred embodiment of the method and apparatus of the present invention has been illustrated in the accompanying Drawings and described in the foregoing Detailed Description, it will be understood that the invention is not limited to the embodiment disclosed, but is capable of numerous rearrangements, modifications and substitutions without departing from the spirit of the invention as set forth and defined by the following claims.