Date of Award

3-2025

Document Type

Thesis

Degree Name

Master of Science in Engineering Management

Department

Department of Systems Engineering and Management

First Advisor

Daniel J. Weeks, PhD

Abstract

Mission readiness in the United States Air Force (USAF) is critically dependent on the sustainment and maintenance of aging facilities. However, budgetary and manpower constraints have led to a significant backlog of deferred maintenance, estimated at $137 billion. To address these challenges, this research develops an optimization model that aids in prioritizing facility maintenance strategies within a fixed budget. The methodology incorporates existing USAF data from BUILDER, NexGenIT, and financial records to create a simulation-based optimization framework at the base level. The model evaluates four different maintenance strategies—replacement only, preventative maintenance only, corrective maintenance only, and a combination of preventative and corrective maintenance—by determining optimal repair and replacement thresholds through a Savings-to-Investment Ratio (SIR) analysis. The model also integrates Tactical Mission Dependency Index (T-MDI) ratings to enhance decision-making at the local commander level. A case study using Moody Air Force Base data demonstrates the model’s effectiveness. Results indicate that incorporating preventative maintenance significantly extends asset lifecycles and optimizes resource allocation. Additionally, prioritization based on T-MDI provides a more mission-relevant allocation of funds compared to the standard USAF prioritization approach. While the current USAF prioritization scheme yielded slightly higher average condition scores, the proposed model better aligned maintenance funding with mission-critical needs. This research highlights the necessity of integrating both strategic-level optimization and base-specific priorities to improve facility sustainment outcomes. It provides a framework for optimizing facility sustainment and maintenance funding and creates a tool that can be used at the base level for decision support.

AFIT Designator

AFIT-ENV-MS-25-M-087

Comments

An embargo was observed for posting this thesis.

This work is marked Distribution A, Approved for Public Release. PA case number 88ABW-2025-0220

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