Systems and methods for prioritizing funding of projects

Patent metadata

Field	Value
Publication number	US-9953284-B2
Application number	US-201313837782-A
Country	US
Kind code	B2
Filing date	Mar 15, 2013
Priority date	Mar 15, 2013
Publication date	Apr 24, 2018
Grant date	Apr 24, 2018

Abstract

Systems and methods for providing a prioritization of the focus and allocation of available resources and/or funding for due diligence analyses of a variety of candidate projects competing for limited funding are disclosed. Various methods may also determine a confidence level metrics associated with the information and/or estimates associated with the candidate projects. Evolutionary algorithms may be applied to perform multi-objective optimization of objectives based, at least in part, on currently available information and/or estimates associated with the candidate projects. A priority score, for the purpose of allocating due diligence attention and resources to increase confidence levels in assumptions associated with candidate projects, may be determined for a particular project based, at least in part, on the current confidence level associated with that particular project and the percentage of non-dominated projects within which the particular project is included. The optimization may be performed multiple times, such as once for every stakeholder that may have provided information and/or estimates associated with the candidate projects, to identify a plurality of non-dominated solutions to the optimization problem.

First claim

That which is claimed:

1. A method, comprising:
receiving, by one or more master processors, an indication of one or more candidate projects, one or more sets of estimates of metrics associated with the one or more candidate projects, and one or more objectives for an initial due diligence of the one or more candidate projects;
allocating, by the one or more master processors and based at least in part on work requests from one or more slave processors, the one or more sets of estimates of metrics associated with the one or more candidate projects to the one or more slave processors;
performing, based at least in part on the one or more sets of estimates of metrics, an evolutionary algorithm to optimize to the one or more objectives, wherein the evolutionary algorithm comprises determining objective values corresponding to the one or more objectives using one or more slave processors;
identifying, by the one or more master processors and based at least in part on performing the evolutionary algorithm, a plurality of non-dominated solutions, the non-dominated solutions representing an optimization according to the one or more objectives;
determining, by the one or more master processors, a respective percentage (P) corresponding to each of the one or more candidate projects, wherein each respective percentage (P) is a percentage of the plurality of non-dominated solutions in which each of the corresponding one or more candidate projects are included;
identifying, by the one or more master processors and based at least in part on the one or more sets of estimates, a respective confidence level (C) corresponding to each of the one or more candidate projects;
generating, by the one or more master processors and based at least in part on the respective percentage (P) and the respective confidence level (C) corresponding to each of the one or more candidate projects, a respective priority score (R=P(I−C)) corresponding to each of the one or more candidate projects;
providing, by the one or more master processors, an indication of each of the respective priority scores corresponding to each of the one or more candidate projects; and
allocating due diligence funding to the one or more candidate projects according to the respective priority score (R) corresponding to the one or more candidate projects.

2. The method of claim 1 , wherein each of the one or more sets of estimates of metrics corresponds to a respective stake holder.

3. The method of claim 1 , wherein the one or more sets of estimates comprises at least one of: (i) expected benefits; (ii) risks associated with the project; (iii) cost estimates; (iv) time to completion; (v) importance to other projects.

4. The method of claim 1 , further comprising receiving one or more constraints associated with the one or more projects.

5. The method of claim 1 , wherein performing the evolutionary algorithm further comprises identifying at least one objective function associated with the one or more objectives.

6. The method of claim 1 , wherein performing the evolutionary algorithm further comprises identifying, by the one or more processors, at least one epsilon value.

7. The method of claim 1 , wherein a respective at least one of the plurality of non-dominated solutions correspond to each of the one or more sets of estimates of metrics.

8. The method of claim 1 , further comprising receiving one or more second confidence levels associated with one or more of the estimates of the one more sets of estimates.

9. The method of claim 8 , wherein the respective confidence level (C) corresponding to each of the one or more candidate projects is based, at least in part, on the one or more second confidence levels.

10. A system, comprising:
a memory that stores computer-executable instructions;
a plurality of processors comprising at least one slave processor and at least one master processor, the plurality of processors configured to access the memory, wherein the plurality of processors are further configured to execute the computer-executable instructions to:
receive, by the at least one master processor, an indication of one or more candidate projects, one or more sets of estimates of metrics associated with the one or more candidate projects, and one or more objectives for an initial due diligence of the one or more candidate projects;
allocate, by the at least one master processor, and based at least in part on work requests from at least one slave processors, the one or more sets of estimates of metrics associated with the one or more candidate projects to the one or more slave processors;
perform, based at least in part on the one or more sets of estimates of metrics, an evolutionary algorithm to optimize to the one or more objectives, wherein the evolutionary algorithm comprises determining objective values corresponding to the one or more objectives using the at least one slave processors;
identify, based at least in part on performing the evolutionary algorithm, a plurality of non-dominated solutions, the non-dominated solutions representing an optimization according to the one or more objectives;
determine, by the at least one master processor, a respective percentage (P) corresponding to each of the one or more candidate projects, wherein each respective percentage (P) is a percentage of the plurality of nondominated solutions in which each of the corresponding one or more candidate projects are included;
identify, based at least in part on the one or more sets of estimates, a respective confidence level (C) corresponding to each of the one or more candidate projects;
generate, based at least in part on the respective percentage (P) and the respective confidence level (C) corresponding to each of the one or more candidate projects, a respective priority score (R=P(I−C)) corresponding to each of the one or more candidate projects;
provide, by the at least one master processor, an indication of each of the respective priority scores corresponding to each of the one or more candidate projects; and
allocate due diligence funding to the one or more candidate projects according to the respective priority score (R) corresponding to the one or more candidate projects.

11. The system of claim 10 , wherein each of the one or more sets of estimates of metrics corresponds to a respective stake holder.

12. The system of claim 10 , wherein the one or more sets of estimates comprises at least one of: (i) expected benefits; (ii) risks associated with the project; (iii) cost estimates; (iv) time to completion; (v) importance to other projects.

13. The system of claim 10 , wherein the plurality of processors are further configured to receive one or more constraints associated with the one or more projects.

14. The system of claim 10 , wherein the plurality of processors configured to perform the evolutionary algorithm further comprises the plurality of processors configured to identify at least one objective function associated with the one or more objectives.

15. The system of claim 10 , wherein the plurality of processors configured to perform the evolutionary algorithm further comprises the plurality of processors configured to identify at least one epsilon value.

16. The system of claim 10 , wherein a respective at least one of the plurality of non-dominated solutions correspond to each of the one or more sets of estimates of metrics.

17. The system of claim 10 , wherein the plurality of processors are further configured to receive one or more second confidence levels associated with one or more of the estimates of the one more se

Assignees

• Aerospace Corp

Inventors

• Smith Patrick L
• Ferringer Matthew Phillip

Classifications

• G06Q10/06313 Primary

  Resource planning in a project environment · CPC title