Air Force Research Laboratory
C. Richard Cassady, Suzan Alaswad, Yisha Xiang, Edward A. Pohl, Rebekah Johnson, Kellie Schneider , Nick Rew
University of Arkansas
This project extends existing selective maintenance models to incorporate important concepts, defines solution approaches for the newly-formulated problems, determines the types of information available to logistic planners through the implementation of AMIT and SMART, and tests solution procedures to develop “rules of thumb” for managing the dynamic selective maintenance scenario
Because all military organizations depend on the reliable performance of repairable systems, the use of mathematical modeling for the purpose of modeling repairable systems and designing optimal maintenance policies for these systems has received an extensive amount of attention in the literature. Because the vast majority of this work ignores potential maintenance resource limitations, models for selective maintenance, the process of identifying the subset of actions to perform from a set of desirable maintenance actions, have recently been developed. The need for these type models will only increase as the military moves to a “sense and respond” logistics command and control capability. Using programs like AMIT and Smart Systems, the maintenance personnel will have increased “situational awareness” on logistics resources (personnel, material availability, failure data). Given this information, logistics planners will need tools that allow them the flexibility to make resource allocation decisions in “near” real time such that “mission readiness” is maximized. This project will provide a modeling framework for a decision-support tool capable of using the “anticipated” information available from programs like AMIT and SMART to dynamically plan the flying schedule (production) under limited maintenance resources (people, and materials). Previously, we have developed a class of mathematical models that can be used to identify selective maintenance decisions for the following scenario – A system has just completed a mission and will begin its next mission soon. Maintenance cannot be performed during missions, therefore the decision-maker must decide which components to maintain prior to the next mission. In previous and ongoing TLI – Military Logistics research efforts, we have extended the original selective maintenance model in two important ways. First, we created a multi-system, single-mission selective maintenance model. Second, we created a single-system, multi-mission model. The objective of this project is to develop a comprehensive selective maintenance model that considers multiple systems and allows for the planning horizon to extend beyond the next future mission. In addition, this model will extend the earlier work by incorporating the new information expected to be available to aid in the decision-making process. Other important concepts such as component aging, imperfect maintenance, cannibalization and workforce shaping will also be considered.