Date of Award

2010

Degree Type

Dissertation

First Advisor

Manbir Sodhi

Abstract

The value of many machines and consumer devices can be extended considerably by planned upgrades of components or parts, contributing to increased sustainability, effectiveness and lower ownership costs. When machines are composed of components that can be replaced independently, they can be considered as complex machines. The performance of such machines is computable as some function of the performance of the constituent components. This concept of operations, sometimes known as modular upgrades, can require the coordination of a large inventory of machines and components in different conditions, sometimes geographically distributed over large areas. This dissertation introduces the concept of an effective inventory as an inventory of interchangeable and upgradeable components acquired to meet a set of goals. Inventory effectiveness can be defined by composing a function that includes key parameters and design characteristics of an inventory of complex machines. These definitions allow us to determine not only the impact of individual components and machines on the goals for the short term planning horizon, but allows an evaluation of various options for the long term planning horizon. The capability of improving the effectiveness of a inventory of complex machines is built upon the foundation of industry and academic research in areas such as modular design, closed-loop supply chain, reliability, design for replacement and other related areas of study. This dissertation will introduce the concept of the five methods or options for improving the condition of individual components and machines. These are Reuse, Repair, Refurbish, Replace/Remanufacture, and Redesign (Five R's). A model for determining replacement decisions for the components of a single and multiple machine over both a single and extended horizon is developed. Models for both fixed and incremental efficiency improvements are developed for an identical configuration of inventory. The models are solved optimally using linear mixed-integer-programming techniques. Heuristics for solution have been proposed. Two case studies are examined using the models developed in this dissertation.

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