Multivariate Manpower, Personnel and Training (MPT) Modeling and Management System

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N/A
Agency Tracking Number: 43901
Amount: $30,000.00
Phase: Phase I
Program: STTR
Awards Year: 1999
Solicitation Year: N/A
Solicitation Topic Code: N/A
Solicitation Number: N/A
Small Business Information
Frontier Technology, Inc.
6785 Hollister Avenue, Goleta, CA, 93117
DUNS: N/A
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 George E Crowder, Jr.
 () -
Business Contact
 Linda A. Sparks
Phone: () -
Research Institution
 Georgia Tech Research Institute
 400 N. 10th Street
Atlanta, GA, 30318
 Nonprofit college or university
Abstract
The Navy would greatly benefit from a computerized system that identifies environmental variables/personnel characteristics that predict accession, trainability, assignment performance, and retention of potential recruits/current personnel. A Bayesian network (BN) methodology is ideally suited to identifying/modeling predictive variables and their impacts over time. In Phase I, we propose using BNs in a multivariate statistical analysis of variables/characteristics that are important to predicting behavior for ten Navy/Marine ratings. The resultant computerized BN model will capture relationships between variables/characteristics and accession, training, performance, and retention behavior of current or future Navy personnel, so that impacts of proposed/hypothesized changes in environmental variables (such as Navy personnel policies), or personnel characteristics could be quickly evaluated. We will also anced human factors engineering principles. Since determining optimal policies by trial and error with the BN model could be cumbersome, our Phase I Option will develop a Genetic Algorithm software component to interface with the BN model to automatically explore and timal policies/characteristics based on user specified optimality criteria (e.g., retention). The option will also develop a plan for validating the system, scaling up to many more ratings, and identifying tradeoff opportunities in Phase II.

* information listed above is at the time of submission.

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