Optimal Location of Navy Recruiters

Abstract

This research has developed and computationally implemented the Navy Recruiter Prediction and Optimization Model (NRPOM). NRPOM can assist Navy Recruiting Command (NRC) with the assignment of recruiters to geographical areas across the U.S. Under given assumptions, NRPOM optimizes: (a) the allocation of a limited number of recruiters to candidate recruiting stations in a region; (b) the assignment of Zip codes to recruiting stations; and (c) the (fraction of) time recruiters should spend at each Zip code. The research has also developed a predictive tool that produces input data for the optimization. Experiments conducted on realistically-sized problems demonstrate that these tools can be used to guide NRCs decisions. However, NRPOM has only been tested with notional data from the state of California, and for this case some of the required inputs have not been provided by NRC; instead, the authors have used estimations that have no guarantee of reflecting actual data. Thus, we believe that NRPOM is a starting point by which to approximate a truly optimal solution to the problem; however its development is not finalized yet.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2018
Accession Number
AD1060266

Entities

People

  • Javier Salmeron
  • Samuel E. Buttrey

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Birds
  • California
  • Computer Programs
  • Data Set
  • Department Of Defense
  • Digital Data
  • Education
  • Governments
  • Guarantees
  • Information Operations
  • Mathematical Models
  • Military Operations
  • Models
  • New York
  • Operating Systems
  • Operations Research
  • Optimization
  • Production Rate
  • Recruiting
  • Recruits
  • Simulations
  • United States

Fields of Study

  • Computer science

Readers

  • Energy Conservation and Renewable Energy Engineering.
  • Naval Personnel Management
  • Operations Research