Date of Award


Document Type


Degree Name

Master of Science (MS)


Industrial Engineering

Committee Chair/Advisor

Emily Tucker

Committee Member

Sudeep Hegde

Committee Member

Thomas Sharkey


This study illustrates the significance of iterative model development using the deployment of hand sanitizer stations in buildings at Clemson University as a case study. The COVID-19 problem affected Clemson University, a major institution, in several ways requiring adaptations to existing policies and procedures to take place. Following guidelines provided by the Centers for Disease and Control (CDC), the university implemented several new strategies including placing hand sanitizer stations in several buildings on campus in order to try and mitigate the transmission of the virus. This study focuses on learning how the initial decision-making took place to then design a representative model that can provide future recommendations. We first use semistructured interviews to understand the historical decisions behind the placement of these dispensers. We then come up with an initial model design strategy to capture what was done by the university. Finally, through ongoing interviews with key stakeholders we use an iterative modeling process to eliminate discordance between the model and the actual decision-making strategies to design a representative model. The thesis will first outline the strategies and techniques that were used to gather qualitative information. It will also present some of the quantitative data that was gathered. Next, the iterative modeling development process will be provided in detail. After this, the models are formally outlined and described. The subsequent results are then presented. Finally, the thesis discusses the takeaways from the iterative modeling process as well as the future plans with regards to implementation of the model. The value of this research study is to show how qualitative research methods like semi-structured interviews with key stakeholders iii can aid the iterative development of optimization models that lead to an ideal representative model to be implemented in the future.



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