Advisor: Dr. Seong-Hee Kim, School of Industrial and Systems Engineering, Georgia Tech
Committee members:
Dr. Sigrun Andradottir, School of Industrial and Systems Engineering, Georgia Tech
Dr. Yajun Mei, School of Industrial and Systems Engineering, Georgia Tech
Dr. Enlu Zhou, School of Industrial and Systems Engineering, Georgia Tech
Dr. Chuljin Park, Dept of Industrial Engineering, Hanyang University
Date and Time: 10am-12pm, Wednesday, June 10th, 2020
Meeting URL (for BlueJeans):
https://bluejeans.com/3466281827
Meeting ID (for BlueJeans):
3466281827
Abstract:
This thesis mainly consists of four parts. The first three parts explore Bayesian methods in solving the feasibility determination problem that commonly arises in the study of simulation and the last part considers spatiotemporal scheduling in manufacturing. More specifically, we propose a new reward function for Bayesian feasibility determination which emphasizes the importance of barely feasible/infeasible systems whose mean performance measures are close to the threshold. We utilize our proposed reward function in developing a Bayesian procedure and show its advantage in comparison with the benchmark procedures in the first part. Then, we present new two-stage and sequential Bayesian procedures that are not only easy to compute but also effective in solving the feasibility determination problem in the second part. In the third part, we focus on solving feasibility determination using a Gaussian process and propose our novel acquisition function. Finally, the last part focuses on a different topic, namely, spatiotemporal scheduling which often occurs in a manufacturing site where products are large and tend to be customized, such as ships, aircraft, and constructional structures. We consider how to generate a reasonably good temporal and spatial schedule on the manufacturing process. We propose a two-phase approach in solving this scheduling problem with an application to block scheduling in shipbuilding.
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