Robotic mobile fulfillment systems automate storage and transportation within fulfillment centers while still relying on human pickers. We focus on optimizing two key performance metrics in these systems: overall completion time and the number of required robots. We investigate two interrelated operational problems influencing these metrics: (i) pod selection, which involves choosing the mobile racks for item picking, and (ii) pod scheduling, which entails assigning these racks to pickers and determining the picking sequence. We show that the pod scheduling problem is NP-hard under both objectives. We compare sequential and integrated approaches for addressing the two problems.

Search advertising constitutes the largest segment of the global digital advertising market, accounting for approximately 40% of total spending. Advertisers typically select between platform-managed bidding and self-managed bidding strategies. We first develop a machine learning-based forecasting framework to predict campaign performance under platform-managed bidding, allowing advertisers to anticipate future outcomes. Then, we introduce an optimization framework to inform bid allocation under self-managed bidding. Our approach offers a decision-support tool for advertisers evaluating a transition from platform-managed bidding to self-managed bidding, with the goal of improving advertising efficiency and performance over time.