PROGRESS MONITORING METHOD

• US 20120290347A1
• Filed: 07/25/2012
• Published: 11/15/2012
• Est. Priority Date: 08/10/2010
• Status: Abandoned Application

• Current Assignee: King Fahd University Of Petroleum And Minerals
• Original Assignee: King Fahd University Of Petroleum And Minerals
• Inventors: ELAZOUNI, ASHRAF, SALEM, OSAMA
• Application Number:
  US13/558,195
• Publication Number:
  US 20120290347A1
• Time in Patent Office: Days
• US Class Current: 705/7.12
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 10/06313 Resource planning in a project environment

Abstract

The progress monitoring method is based on a critical path method (CPM) and conducts comparisons against multiple possible outcomes utilizing neural networks that classify planned progress at specified cut-off dates during a planning stage. The classifications are used to monitor and evaluate actual progress during the construction stage. The pattern recognition techniques generalize a virtual benchmark to represent planned progress based on multiple possible outcomes generated at each cut-off date. The generalization feature overcomes the problem of variation in the quality of data collected. Patterns are constructed to encode planned and actual progress at different cut-off dates. Patterns are readily manipulated within computer programs and substitute for photographs, which are not comprehensive in representing the work status of interior and hidden parts of the under-construction facilities.

Claims

1. A computer software product that includes a storage medium readable by a processor, the storage medium having stored thereon a set of instructions for performing monitoring of progress schedules, the instructions comprising:

(a) a first set of instructions which, when loaded into main memory and executed by the processor, causes the processor to build a Critical Path Method (CPM) schedule of a project;

(b) a second set of instructions which, when loaded into main memory and executed by the processor, causes the processor to map, during a planning stage of the project, pattern sets of cut-off dates of the project to the CPM schedule;

(c) a third set of instructions which, when loaded into main memory and executed by the processor, causes the processor to identify, during the planning stage, project cut-off date weeks corresponding to the pattern sets of the project cut-off dates;

(d) a fourth set of instructions which, when loaded into main memory and executed by the processor, causes the processor to apply the pattern sets and corresponding project cut-off date weeks as inputs to a neural network pattern recognition model of a Hopfield network;

(e) a fifth set of instructions which, when loaded into main memory and executed by the processor, causes the processor to use at least one of the generated patterns to train the neural network pattern recognition model to classify work planned at specified cut-off dates;

(f) a sixth set of instructions which, when loaded into main memory and executed by the processor, causes the processor to use the remaining patterns to test the neural network pattern recognition model after it has been trained;

(g) a seventh set of instructions which, when loaded into main memory and executed by the processor, causes the processor to monitor the project, during the construction stage of the project, at the same cut-off dates;

(h) an eighth set of instructions which, when loaded into main memory and executed by the processor, causes the processor to prepare, at any desired cut-off date, a corresponding descriptive pattern, the corresponding descriptive pattern describing actual work accomplishments during a time period defined by the desired cut-off date;

(i) a ninth set of instructions which, when loaded into main memory and executed by the processor, causes the processor to input the descriptive pattern to the neural network pattern recognition model, the model declaring a week of convergence for the descriptive pattern input;

(j) a tenth set of instructions which, when loaded into main memory and executed by the processor, causes the processor to compare the week of convergence declared by the neural network pattern recognition model to the cut-off date week of the associated cut-off date pattern set, thereby determining whether actual progress of the project is on schedule, ahead of schedule, or behind schedule;

(k) an eleventh set of instructions which, when loaded into main memory and executed by the processor, causes the processor to generate a progress monitoring report based upon the determined actual progress; and

(l) a twelfth set of instructions which, when loaded into main memory and executed by the processor, causes the processor to display the progress monitoring report to a user.

2. The computer software product according to claim 1, wherein the fifth set of instructions further comprises using a high-speed neural network pattern recognition model training algorithm.

3. The computer software product according to claim 1, wherein the fourth set of instructions further comprises using a neural network pattern recognition model having a single hidden layer.

4. The computer software product according to claim 3, wherein the fourth set of instructions further comprises using approximately forty-three neurons in said single hidden layer.

5. The computer software product according to claim 1, further comprising a thirteenth set of instructions which, when loaded into main memory and executed by the processor, causes the processor to benchmark the entire project based on multiple possible outcomes generated by said neural network pattern recognition model at each said cut-off date.

6. The computer software product according to claim 1, further comprising a fourteenth set of instructions which, when loaded into main memory and executed by the processor, causes the processor to associate an output pattern including a vector having a number of elements equal to the total number of project weeks with each input pattern.

7. The computer software product according to claim 1, further comprising a fifteenth set of instructions which, when loaded into main memory and executed by the processor, causes the processor to construct additional patterns at each cut-off date, the additional patterns being generated by randomly assigning values to the activities'"'"' start times within a range of an early start time (EST) and a late start time (LST), while maintaining a sequence of the activities, the additional patterns representing multiple possible patterns leading to the same project duration;

wherein sets of random patterns at all the specified cut-off dates along with their corresponding weeks constitute inputs to feed to the neural network pattern recognition model.

8. The computer software product according to claim 1, wherein the fifth set of instructions further comprises constructing a plurality of training pattern groups, each training pattern group of the plurality of training pattern groups being uniquely associated with each interval of the longest time period shown in the CPM schedule, the training pattern groups being split further into a first number of sub-groups and a second number of sub-groups, individual patterns of the first number of sub-groups being used for updating the neural network weights and biases while being entered randomly to the neural network, the second number of sub-groups being used for validation.

9. The computer software product according to claim 8, further comprising a sixteenth set of instructions which, when loaded into main memory and executed by the processor, causes the processor to validate the neural network pattern recognition model, the validation including a stopping criterion such that when a pattern recognition error first begins to increase, the training session is stopped, and weights and biases of the neural network pattern recognition model corresponding to a minimum pattern recognition error value are returned.

10. The computer software product according to claim 8, further comprising a seventeenth set of instructions which, when loaded into main memory and executed by the processor, causes the processor to validate the neural network pattern recognition model, the validation including a stopping criterion, wherein training continues until a maximum number of 50 epochs occurs.

11. The computer software product according to claim 9, wherein the minimum pattern recognition error is less than about 1×

10<sup>−

8</sup>.

12. A computerized progress monitoring method carried out on a computer programmed to implement a Hopfield neural network, comprising the steps of:

building a Critical Path Method (CPM) schedule of a project;

mapping, during a planning stage of the project, pattern sets of cut-off dates of the project to the CPM schedule;

identifying, during the planning stage, project cut-off date weeks corresponding to the pattern sets of the project cut-off dates;

applying the pattern sets and corresponding project cut-off date weeks as inputs to a neural network pattern recognition model on the computer;

using at least one of the generated patterns to train the neural network pattern recognition model on the computer to classify work planned at specified cut-off dates;

using the remaining patterns to test the neural network pattern recognition model on the computer after it has been trained;

monitoring the project, during the construction stage of the project, at the same cut-off dates;

preparing, at any desired cut-off date, a corresponding descriptive pattern, the corresponding descriptive pattern describing actual work accomplishments during a time period defined by the desired cut-off date;

inputting the descriptive pattern to the neural network pattern recognition model on the computer, the model declaring a week of convergence for the descriptive pattern input; and

comparing the week of convergence declared by the neural network pattern recognition model to the cut-off date week of the associated cut-off date pattern set thereby, indicating whether actual progress of the project is on schedule, ahead of schedule, or behind schedule.

13. The progress monitoring method according to claim 12, wherein the neural network pattern recognition model has a single hidden layer.

14. The progress monitoring method according to claim 13, further comprising the step of using approximately forty-three neurons in said single hidden layer.

15. The progress monitoring method according to claim 12, further comprising the step of benchmarking the entire project based on multiple possible outcomes generated by said neural network pattern recognition model on the computer at each said cut-off date.

16. The progress monitoring method according to claim 12, further comprising the step of associating an output pattern including a vector having a number of elements equal to the total number of project weeks with each input pattern.

17. The progress monitoring method according to claim 12, further comprising the step of constructing additional patterns at each cut-off date, the additional patterns being generated on the computer by randomly assigning values to the activities'"'"' start times within a range of an early start time (EST) and a late start time (LST), while maintaining a sequence of the activities, the additional patterns representing multiple possible patterns leading to the same project duration;

wherein sets of random patterns at all the specified cut-off dates along with their corresponding weeks constitute inputs to feed to the neural network pattern recognition model.

18. The progress monitoring method according to claim 12, wherein the training step further comprises the step of constructing a plurality of training pattern groups, each training pattern group of the plurality of training pattern groups being uniquely associated with each interval of the longest time period shown in the CPM schedule, the training pattern groups being split further into a first number of sub-groups and a second number of sub-groups, individual patterns of the first number of sub-groups being used for updating the neural network weights and biases while being entered randomly to the neural network on the computer, the second number of sub-groups being used for a validating step.

19. The progress monitoring method according to claim 18, further comprising the step of validating the neural network pattern recognition model on the computer, the validating step including a stopping criterion such that when a pattern recognition error first begins to increase, the training session is stopped, and weights and biases of the neural network pattern recognition model corresponding to a minimum pattern recognition error value are returned.

20. The progress monitoring method according to claim 18, further comprising the step of validating the neural network pattern recognition model using the computer, the validating step including a stopping criterion wherein training continues until a maximum number of 50 epochs occurs.