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Thesis Defence: The Value of Text for Waiting Time Prediction Using Deep Learning: A Repair Shop Case Study
August 23 at 2:00 pm - 5:00 pm
Mohammad Mosaffa, supervised by Dr. Javad Tavakoli, will defend their thesis titled “The Value of Text for Waiting Time Prediction Using Deep Learning: A Repair Shop Case Study” in partial fulfillment of the requirements for the degree of Master of Science in Mathematics.
An abstract for Mohammad Mosaffa’s thesis is included below.
Defences are open to all members of the campus community as well as the general public. Please email firstname.lastname@example.org to receive the Zoom link for this defence.
One of the most formidable tasks for service companies is to inform customers about the approximate waiting time. Overestimated waiting time may force customers not to proceed with a company, and underestimated waiting time may lead to an impossible responsibility for the company to accomplish. In both cases, customer dissatisfaction would occur. To tackle this challenge, we propose a two-phases predictive model using textual combined with structured data to predict waiting time in service systems. In the first phase, the Bag of Words as a text mining approach is utilized to transform texts into numbers. In the second phase, based on nearly 31,000 case study data, our work exploits a Multi-Layer Perceptron architecture from deep learning as the best predictive model for a regression task. Our numerical results show that deep learning by having 87.6% accuracy has the best performance compared to conventional machine learning algorithms for predicting a continuous output based on textual data. Also, our work illustrates the value of texts in waiting time prediction since results show that not only using solely textual data provides 3.6% additional accuracy but also combining structured and textual data is the best due to having 5.3% additional accuracy compared to using solely structured data. Finally, we demonstrate that our predictive model can assist experts’ decisions in underestimated waiting time cases by improving 50.1% accuracy. Also, from the managerial perspective, we illustrate how textual data are vital in terms of descriptive analysis to find deficiencies in a service system.