An Intelligent Decision Support System for Workforce Forecast

Abstract

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information , 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY) 05-01-2011 REPORT TYPE 7184D214 PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBERThe University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)Air SPONSOR/MONITOR'S REPORT NUMBER(S)AFRL-RH-WP-TR-2011-0019 DISTRIBUTION / AVAILABILITY STATEMENTDistribution A: Approved for public release; distributed is unlimited. SUPPLEMENTARY NOTES88ABW/PA cleared on 23 Feb 2011, 88ABW-2011-0727. ABSTRACTThe main objective of this project is to enhance the effectiveness of workforce forecast and deployment through innovative approaches of artificial intelligence. The research included a final document of conventional and innovative methods of workforce forecasting and a decision support software program incorporating advanced artificial intelligence techniques for workforce forecasting. SUBJECT TERMS ABSTRACTThis project studies workforce forecasting in two main aspects: (1) an extensive review of the existing methodologies and techniques and (2) an effort to develop a decision support system with models and software programming. The main objective of this project is to enhance the effectiveness of workforce forecasting and deployment through innovative approaches of artificial intelligence. The deliverables include a summary report of conventional and innovative methods of workforce forecasting (Part I) and a decision support software program using artificial intelligence techniques for workforce forecasting (Part II).Part I, provided a thorough literature review of fundamental research and practices of demand and supply forecasting techniques for workforce analysis. Over 300 relevant literatures have been identified and reviewed by the project team, and 289 of them are covered in this report.Following is a summary of the main contents and contributions of Part I:• This report provides an extensive review of the fundamental knowledge, applications, guidelines, and scope of use of various workforce forecasting methods.• A decision tree has been proposed for selecting an appropriate forecasting ...

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“…The results of the experiment showed that the application of fuzzy logic in this method had improved the precision, perceived quality and the performance on the metadata field. Wan et al [11] presented an intelligent decision system for stock managers based on the fuzzy inference which can assist in deciding that people buy, sell or hold their stock. Fuzzy-based clustering was introduced in another study [12].…”