We study the problem when a firm sets prices for products based on the transaction data, i.e., which product past customers chose from an assortment and what were the historical prices that they observed. Our approach does not impose a model on the distribution of the customers’ valuations and...

Operational models often balance assignment and capacity decisions under data uncertainty. For example, in operating room scheduling the decision maker must define appropriate limits for personnel overtime while accounting for random surgery durations. Typical approaches to hedge against...

We present relaxations for discrete optimization problems using approximate linear programs (ALPs) defined on multiple networks that represent different state-space aggregations. Our network ALP leverages information across these networks using a piecewise-constant value function approximation...