This capstone project evaluates the application of stochastic optimization techniques in middle-mile transportation planning, incorporating historical variance in transportation time and yard dwell time. Wayfair's current middle-mile planning process uses advanced forecasting and optimization techniques, but it struggles to account for the randomness and variation of the real world. To address this, the capstone project evaluates whether incorporating sources of variance into the optimization process can outperform traditional models in generating resilient transportation plans. After analyzing 70,000 trips from January 2022 to January 2023, three significant lanes were selected to evaluate changes in the distributions based on day of the week, season, and carrier. For each lane, 20 simulated transportation plans were created using stochastic data. Results confirm that accounting for variance improves outcomes, showing the possibility to incorporate more realistic inputs to the transportation planning process. Managerial insights highlight the model's possibility to representing real-world scenarios, enabling informed decisions on resource allocation, route selection, and scheduling. The scenario-based approach balances speed and efficiency, empowering organizations to manage uncertainty.