PhD Oral Exam - Hoora Katoozian, Industrial Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

The Fourth Industrial Revolution, or Industry 4.0 (I4.0), has transformed manufacturing by integrating cyber-physical systems, artificial intelligence, and the Internet of Things, enabling highly flexible and customer-centric production systems. Central to this paradigm is mass personalization (MP), where products are customized to individual specifications, particularly in high-tech sectors such as aerospace, medical devices, and precision optics. These industries demand resilient supply networks to manage low-volume production, complex customizable designs, and uncertainties in customer requirements and supplier capabilities. Traditional supply chain models struggle to address these dynamics, highlighting the need for advanced optimization frameworks to ensure cost efficiency, short lead times, and high service levels.

This thesis investigates the design of resilient and reconfigurable supply networks for MP in I4.0, with a focus on high-tech industries producing modular, customizable products. The research delivers three key contributions. First, a strategic mixed-integer programming (MIP) model is proposed to optimize supplier selection and order allocation in a reconfigurable supply network, balancing design complexity and economies of scale to maximize profit and service level. Second, a two-stage stochastic programming (2SP) model is developed for platform-based manufacturing networks, incorporating crowdsourcing to enhance resilience against supplier failures by selecting primary and backup suppliers under uncertain technological capabilities. Third, an adjustable robust optimization (ARO) model is formulated for multi-echelon manufacturing networks, ensuring robustness against supplier capacity variability and bill-of-material complexity, supported by an efficient math-heuristic algorithm.

Validation through extensive numerical experiments, including sensitivity analyses and Monte Carlo simulations, demonstrates the models' effectiveness in mitigating risks and enhancing network resilience. This thesis offers practical insights for high-tech industries, providing a comprehensive framework for designing agile, cost-effective supply networks capable of meeting the demands of mass personalization in the I4.0 era.