Date: 06/23/2023
Time: 9:00 am - 11:00 am (EST)
Location: Microsoft Teams
Meeting ID: 270 977 525 472
Passcode: HRMkN4
Chin-Yuan Tseng
Industrial Engineering PhD Student
School of Industrial and Systems Engineering
Georgia Institute of Technology
Committee:
Dr. Ben Wang (advisor), Industrial and Systems Engineering, Georgia Tech
Dr. Chip White (advisor), Industrial and Systems Engineering, Georgia Tech
Dr. Xiaoli Ma (advisor), Electrical and Computer Engineering, Georgia Tech
Dr. Chuck Zhang, Industrial and Systems Engineering, Georgia Tech
Dr. Kan Wang, Georgia Tech Manufacturing Institute, Georgia Tech
Abstract:
This thesis advances the field of digital supply chain management by proposing and implementing a simulation-based decision support platform for autologous cell-derived medical products (AuCMP). The research focuses on three topics: i) modeling AuCMP supply chains to improve patient benefits and optimize strategies, including decentralized manufacturing and resource sharing in decentralized networks; ii) incorporating patient/therapy data into manufacturing decision-making; and iii) AI-augmented decision support by integrating reinforcement learning algorithms with simulation.
In Chapter 2, the study introduces a novel simulation platform, AuCMP-DSS, designed for AuCMP supply chain decision support. AuCMP-DSS employs agent-based and discrete-event simulation approaches to model the macro- and micro-scale activities in the targeted supply chain. In addition, we proposed a modular approach to enable the model's generalizability for various products. Case studies are conducted to illustrate the platform's utility, particularly in comparing inventory costs and optimizing supply chain capacity. The platform is further used to analyze the trade-offs between centralized and decentralized AuCMP supply chains in the USA context.
Chapter 3 explores the importance of real-time patient health data in CAR T-cell therapy manufacturing decisions. By integrating a System Dynamics simulation module into the AuCMP-DSS platform, the research demonstrates the potential benefits of adjusting the manufacturing process based on individual patient health status and prioritizing patients during therapy manufacturing. The potential improvements include enhanced survival rates, increased sales, and greater profits.
In Chapter 4, the research confronts two main challenges within decentralized AuCMP manufacturing networks: production job dispatching and capacity planning. Data-driven approaches employing simulation and reinforcement learning algorithms are proposed, and their effectiveness in achieving superior performance under various scenarios is demonstrated.
The emerging AuCMP industry and its expected market growth in the near future emphasize the importance of these proposed methodologies. This dissertation ultimately provides innovative strategies for planning and managing the AuCMP supply chain, facilitating successful commercialization and meet anticipated demand.
Chapter 5 concludes the thesis by summarizing the main findings and opening up potential avenues for future research
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