The main challenges of electronics supply chains include unpredictable customized demands, short product lifecycles, high inventory costs, and long lead-times. To handle these challenges and provide rapid responses to customer orders, it is necessary to determine an effective long-term risk mitigation strategy for these businesses. This book chapter proposes a risk-based optimization framework for electronic supply chains that adopts a hybrid fabrication–fulfillment manufacturing approach. The problem is modeled as a two-stage stochastic model that determines the best strategies for supplier selection, capacity allocation, and assembly lines placement considering the risks associated with demand uncertainty, supply interruptions, delays, and quality and equipment failures. The proposed solution method integrates learning with optimization techniques where artificial network is used to reduce search time of the stochastic optimization model. A case study for an integrated supply chain of high-end server manufacturing is used to illustrate the validity of the model and assess the quality and robustness of the solutions obtained by this technique.