Degree Type

Dissertation

Date of Award

2020

Degree Name

Master of Science

Department

Industrial and Manufacturing Systems Engineering

Major

Industrial Engineering

First Advisor

Cameron MacKenzie

Abstract

With the growth of globalization in the supply chain industry, manufacturing firms and suppliers are more susceptible to disruptions. There is a huge gap between optimization, simulation, and supply chain risk management. Our research is an attempt to bridge this gap, by improving a currently existing supply chain disruption model through Bayesian optimization technique. During a disruption, suppliers of manufacturing firms do not always have an option of moving their facility to an alternate location. This model optimizes a complex simulation to help identify the optimal risk management strategies for a firm who is planning for a severe supply chain disruption. The results of the model are depicted through an illustrative example based out of the 2011 Japanese earthquake and tsunami, and its robustness is tested through sensitivity analysis. Firms need to be prepared for disruptive events and may have the desire to maximize their profit and this model provides techniques to the decision maker to choose cost-effective strategies based on certain parameters.

DOI

https://doi.org/10.31274/etd-20210114-107

Copyright Owner

Steve Paul

Language

en

File Format

application/pdf

File Size

43 pages

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