We study dual sourcing under non-stationary demand. The actual demand distribution is not observable, yet demand observations reveal partial information about it. We propose a novel policy that combines a base order on the slow source with flexible orders from the fast and slow source. We formulate the problem as a partially observable Markov decision process and prove the optimal policy structure. Our results show how partial demand information and flexible slow source orders may reduce costs.

We partner with a major online retailer to optimize congestion-aware task assignment in robotic warehousing. We develop an original integer optimization formulation using a time-space network representation of fulfillment center operations. To solve it, we develop a machine-learning-guided large-scale neighborhood search algorithm to iteratively construct and re-optimize small subproblems. We demonstrate the benefits of our model and algorithm as compared to baseline algorithms and heuristics.

We study the structural properties of the optimal ordering policy for perishable products by considering demand forecasts in the inventory model. The optimal ordering policy is a state-dependent base-stock policy that depends on the inventory level, current and previous forecasts, and previous demands. We also propose a linear heuristic that integrates demand forecasts in the ordering policy. Despite the simplicity of the heuristic, its performance is comparable to that of the optimal policy.