Strategic hub location decisions are fundamental to the architecture of transportation and supply chain networks, as they directly dictate cost-efficiency, operational flexibility, and overall network performance. This paper introduces a two-stage, bi-objective stochastic programming model designed to address the complexities of network planning. One of the key features of this model is its ability to take into account uncertainty in hub status, considering scenarios in which hubs may be operational or non-operational, while also allowing for direct shipments between selected origin–destination pairs to improve routing efficiency. Formulated as a mixed-integer programming problem, the model navigates uncertainty by evaluating a range of potential scenarios. The framework operates in two distinct phases: the first phase determines strategic hub placement, while the second optimizes tactical decisions, including flow allocation and demand management, within each scenario. This dual-layered approach not only enables a rigorous assessment of network risk and performance under varying conditions but also strikes a balance between minimizing capital and operational expenditures and maximizing service reliability. Our findings demonstrate that integrating hub-and-spoke connectivity with direct routing options yields a significantly more resilient and efficient network, capable of maintaining performance even when hubs become non-operational.