We study a single-item multi-warehouse multi-location order fulfillment problem faced by an online retailer with limited logistic capacity. Orders shall be fulfilled within the time window before being lost. We proposed a heuristic policy based on Lagrangian relaxations and showed that it is asymptotically optimal when the retailer serves a large number of locations. We also showed that a "two-day fulfillment" strategy, together with the policy, could mitigate the shortage of logistic resources.

We examine how an online grocer's fulfillment options affect customers' weekly and lifetime spending. Based on our results, we develop a structural model that estimates the preferences underlying customers’ choices while learning customer preferences. Based on a counterfactual analysis, we recommend whether the retailer should offer pick-up, delivery, or both services in each geographic market.

In this work, we propose an approach that leverages historical customer purchase conversion rates when designing a middle-mile consolidation network that aims to maximize the profit of large e-commerce retailers. We embed lead-time dependent sales volume predictions into a new mixed-integer program (MIP) that simultaneously determines shipment lead times and consolidation plans to maximize profit.