Design and process scheduling optimization method

• US 20110060624A1
• Filed: 09/09/2009
• Published: 03/10/2011
• Est. Priority Date: 09/09/2009
• Status: Active Grant

• Current Assignee: King Fahd University of Petroleum & Minerals (Government of Saudi Arabia)
• Original Assignee: King Fahd University of Petroleum & Minerals (Government of Saudi Arabia)
• Inventors: Elazouni, Ashraf M.A., Abdel-Wahhab, Osama
• Granted Patent: US 8,260,648 B2
• Time in Patent Office: Days
• US Class Current: 705/8
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

The design and process scheduling optimization method is a technique that circumvents problems encountered in previous optimization techniques. The method performs comparison against multiple possible outcomes rather than a single-valued benchmark. Pattern Recognition (PR) techniques are utilized to classify the work planned at specified cut-off dates during the planning stage. Classification is used to monitor and evaluate the progress during the construction stage. The PR technique generalizes a virtual benchmark to represent the whole project based on multiple possible outcomes generated at a given cut-off date. The generalization feature offers a potential tool to overcome the problem of variation in the quality of data collected. Patterns are constructed to encode work of the project at different cut-off dates. The present invention utilizes a robust pattern recognition method applied to Critical Path Method (CPM) procedures to monitor and evaluate progress of construction projects.

Claims

1. A computerized design and process scheduling optimization method, comprising the steps of:

building a CPM schedule of a project;

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

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

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

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

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

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

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

inputting said descriptive pattern to said neural network pattern recognition model which, responsively, declares a week of convergence that said descriptive pattern input tends to converge; and

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

2. The computerized design and process scheduling optimization method according to claim 1, further comprising the step of utilizing a high speed neural network pattern recognition model training algorithm.

3. The computerized design and process scheduling optimization method according to claim 1, further comprising the step of utilizing a neural network pattern recognition model having a single hidden layer.

4. The computerized design and process scheduling optimization method according to claim 3, further comprising the step of utilizing only ten neurons in said single hidden layer.

5. The computerized design and process scheduling optimization method according to claim 1, further comprising the step of benchmarking the entire project based on multiple possible outcomes generated by said neural network pattern recognition model at each said cut-off date.

6. The computerized design and process scheduling optimization method according to claim 1, further comprising the step of associating an output pattern, said output pattern including a vector having a number of elements which equals the total number of project weeks, with each input pattern.

7. The computerized design and process scheduling optimization method according to claim 1, further comprising the step of constructing additional patterns at each cut-off date, said 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 said activities, said additional patterns representing multiple possible patterns which will lead to the same project duration;

and

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 computerized design and process scheduling optimization method according to claim 1, 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, said 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 said neural network weights and biases while being entered randomly to said neural network, said second number of sub-groups being used for a validating step.

9. The computerized design and process scheduling optimization method according to claim 8, further comprising the step of validating said neural network pattern recognition model, said 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.