Abstract

The Fourth Industrial Revolution (Industry 4.0 or I4.0) is transforming manufacturing through the integration of cyber-physical systems, artificial intelligence, and the Internet of Things. At its core is mass personalization (MP), enabling the production of customized products, particularly in high-tech sectors such as aerospace, medical devices, and precision optics. These industries require resilient supply networks to handle low-volume, high-complexity production and uncertainties in customer demands and supplier performance. Traditional supply chain models fall short in addressing these challenges, calling for advanced optimization frameworks.
This thesis explores the design of resilient and reconfigurable supply networks tailored to MP under I4.0. It makes three primary contributions. First, a strategic mixed-integer programming (MIP) model is proposed for optimizing supplier selection and order allocation, balancing design complexity with economies of scale. Second, a two-stage stochastic programming (2SP) model is developed for platform-based manufacturing networks, integrating crowdsourcing to enhance resilience by assigning primary and backup suppliers under uncertain capabilities. Third, an adjustable robust optimization (ARO) model is introduced for multi-echelon networks, addressing variability in supplier capacity and bill-of-material complexity, supported by an efficient math-heuristic algorithm.
Extensive numerical experiments and sensitivity analyses validate the models’ effectiveness in mitigating risk and improving resilience. This research offers actionable insights for high-tech manufacturers aiming to build agile, cost-efficient supply networks that meet the evolving demands of mass personalization in the era of Industry 4.0.