The United States Army's Multi-Period Optimal Readiness Allocation Model

Abstract

The U.S. Army has undergone unprecedented change in the last decade, completing an organizational transformation and redesigning its deployment policies. These changes and other factors have resulted in an increase of 107% in equipping requirements between 2003 and 2011, forcing the Army to update its equipping policies. We develop the multi-period optimal readiness allocation model (MPORAM) to maximize unit equipment readiness across the force over several years. MPORAM extends an earlier single-period model to account for the dynamic nature of unit priorities, budget, and other factors that vary over the planning horizon. Using a small test case, we observe that MPORAM distributes and/or transfers equipment in anticipation of future demand needs. For example, if one unit cannot improve its readiness in one time period, MPORAM focuses on improving other units ratings, if possible, regardless of their priorities. Using two realistically-sized cases, we observe that the multi-period solution does not differ notably from the single-period solution. Thus, we cannot make any strong conclusions about the added value of MPORAM in these cases. However, these results are strongly influenced by a large gap between supply and demand, and we expect MPORAM to improve the single-period solution in more balanced cases.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2011
Accession Number
ADA547918

Entities

People

  • Jay Persons

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • C4I
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accounting
  • Armored Personnel Carriers
  • Computer Programming
  • Data Sets
  • Deployment
  • Engineers
  • Maintenance
  • Mathematical Models
  • Mathematical Programming
  • Military Police
  • Military Science
  • Operations Research
  • Optimization
  • Personnel Management
  • Systems Engineering
  • Training
  • United States

Readers

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  • Operations Research