Year of Publication

2013

Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation

College

Engineering

Department

Electrical Engineering

First Advisor

Dr. Lawrence E. Holloway

Second Advisor

Dr. Jingshan Li

Abstract

The resilience of manufacturing enterprises is an important research topic, since disruptions have severe effects on the normal operation of manufacturing enterprises, especially as manufacturing supply chains become global. Although many case studies have been carried out to address resilience in organizations, a systematic method to model and analyze the resilience dynamics in manufacturing enterprises is not well developed. This study is intended to conduct research on quantitative analysis and control for resilience.

After reviewing the literature addressing resilience, a modeling framework is presented to characterize the resilience of a manufacturing enterprise responding to disruptive events, which includes inventory ow between enterprise nodes, different costs, resource, demand, etc. Each node within the network is represented as a dynamic model with associated costs of production and inventory. This mathematical model is the foundation of quantitative analysis and control. With this model, an optimal control problem is formulated, by which the control can be solved to achieve minimum cost.

Several different types of systems are defined and analyzed in this work. We develop the approach of aggregation to simplify the network structures. The study is mainly focused on two categories of network systems: serial network systems and assembly tree network systems. The analysis on these two categories covers two conditions: in discrete time domain without considering capacities, and in continuous time domain with considering capacities. The methods to determining optimal operations are developed under different conditions. In the serial network systems analysis, a practical case study is introduced to show the corresponding method developed. Finally, the problems are discussed for future research.

Based on the results of these analyses, we present optimal control policies for resilience. Our method can support the analysis of the impact of disruptions, and the development of control strategies that reduce the impact of the disruption.

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