Robotic mobile fulfillment (RMF) systems automate storage and transportation within fulfillment centers while still relying on human pickers. We focus on two performance metrics in these systems, aiming to minimize overall completion time (OCT) as the primary objective and the number of required robots (NRR) as a secondary objective. We investigate two interrelated operational problems influencing these metrics: (i) pod selection and (ii) pod scheduling. We first explore the pod scheduling problem independently, providing theoretical results. This stand-alone problem is NP-hard with at least two pickers when minimizing OCT and remains NP-hard with even one picker when minimizing NRR. The NRR objective introduces a novel optimization problem structure, contributing to scheduling theory. We demonstrate that a simple but effective scheduling rule is asymptotically optimal for minimizing OCT with multiple pickers. For NRR minimization with a single picker, we derive theoretical performance bounds for two sequencing rules. When incorporating the pod selection problem, we focus on two approaches: (i) sequential and (ii) integrated pod selection and scheduling. The sequential approach handles pod selection first, then pod scheduling, while the integrated approach addresses both problems simultaneously within a single formulation to minimize OCT. Computational experiments using e-commerce order data reveal that the sequential approach achieves significantly faster computation times, with a mean OCT similar to that of the integrated approach. We also find that our sequencing rules can reduce mean NRR by 13% to 28% without affecting OCT, compared to the case where no such rules are used.

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.