We study a multi-period dynamic pricing problem with contextual information where the seller uses a misspecified demand model. The seller sequentially observes past demand, updates model parameters, and then chooses the price for the next period based on time-varying features. We show that model...

We consider a price-based network revenue management problem where a retailer aims to maximize revenue from multiple products with limited inventory over a finite selling season. As common in practice, we assume the demand function contains unknown parameters, which must be learned from sales...

In a dynamic pricing problem where the demand function is not known a priori, price experimentation can be used as a demand learning tool. Existing literature usually assumes no constraint on price changes, but in practice sellers often face business constraints that prevent them from conducting...

We study a hybrid strategy that uses both process flexibility and finished goods inventory for supply chain risk mitigation. The interplay between process flexibility and inventory is modeled as a two-stage robust optimization problem. In the first stage, the firm allocates inventory before...

We study an inventory allocation problem in a two-echelon (single-warehouse multiple-retailer) setting with lost sales. At the start of a finite selling season, a fixed amount of inventory is available at the warehouse, and can be allocated to the retailers over the course of the selling horizon...

Airline data have shown that the fraction of customers who choose the cheapest available fare class often is much greater than that predicted by the multinomial logit (MNL) choice model calibrated with the data. For example, the fraction of customers who choose the cheapest available fare class...