Graduation Year

2010

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Industrial and Management Systems Engineering

Major Professor

Alex Savachkin, Ph.D.

Co-Major Professor

Tapas K. Das, Ph.D.

Committee Member

Jose Zayas-Castro, Ph.D.

Committee Member

Alex Volinsky, Ph.D.

Committee Member

Yuncheng You, Ph.D.

Keywords

enterprise networks, lean, capacity disruptions, countermeasure policies, pandemic influenza

Abstract

This work is a compilation of four manuscripts, three of which are published and one is

in the second round of review, all in refereed journals. All four manuscripts focus on analysis

of stochastic disruptions to support design of capacitated engineered networks. The work

is motivated by limited ability to mitigate elevated risk exposure of large-scale capacitated

enterprise networks functioning in lean environments. Such inability to sustain enterprise

capacity in the face of disruptions of various origins has been causing multi-billion enterprise

forfeitures and hefty insurance premiums. At the same time, decision support methodologies

for reliable design of dynamic capacitated networks have been largely unavailable.

This work is organized as follows.

Paper 1

presents a methodology to analyze ca-

pacitated healthcare supply chains using a framework of forward ow-matching networks

with multiple points of delivery. Special emphasis is given to developing stochastic models

for capturing capacity trajectories at the points of delivery.

Paper 2

focuses on assuring

capacity availability for a critical vertex exposed to random stepwise capacity disruptions

with exponentially distributed interarrival times and uniformly distributed magnitudes. We

explore two countermeasure policies for a risk-neutral decision maker who seeks to maxi-

mize the long-run average reward. We present an extensive numerical analysis as well as

a sensitivity study on the uctuations of some system parameter values.

Paper 3

extends

the capacity assurance analysis for critical vertices by considering stepwise partial system

capacity loss accumulating over time. We examine implementation of a countermeasure

policy, aimed at reducing the disruption rate, for a risk-neutral decision maker who seeks to

maximize long-run average return. We explore how the policy of maintaining the optimal

disruption rate is a
ected by a number of system parameters. Finally,

Paper 4

presents a

dynamic predictive methodology for mitigation of cross-regional pandemic outbreaks which

can be used to estimate workforce capacity loss for critical vertices due to such societal

disasters.

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