In recent years, large-scale optimization with emphasis on transportation applications has become a particularly exciting field of research, thanks to the rapid technological advancement in transportation equipment and computational tools. Focusing on previously studied problems with this new perspective, as well as defining and solving new problems inspired by these developments provide valuable insights. This talk addresses modern challenges in transportation and logistics planning from both methodological and practical standpoints. In the first part of this talk, we present a robust optimization model for facilitating empty repositioning of mobile resources in large-scale transportation networks, where demand is uncertain. We extend the "budget of uncertainty" idea to realistically represent empty repositioning settings, and propose a rolling horizon framework featuring this model. We then conduct a comprehensive computational study to demonstrate the effectiveness of the proposed approach. In the second part of this talk, we provide a method for computing dual bounds for multistage stochastic mixed integer programming problems with a finite number of scenarios. These dual bounds, called "partition bounds", are based on solving group subproblems on partitions of the scenario set. We develop a scenario set partition sampling method to obtain effective bounds, and demonstrate the power of this method against another sampling-based approach (Sample Average Approximation) on a wide range of test instances.