This paper considers the problem of hedging inventory risk for a seasonal product whose demand is sensitive to weather conditions, such as the average seasonal temperature. The newsvendor not only decides the order quantity, but also adopts a weather hedging strategy. A typical hedging strategy...

We develop the first nonparametric learning algorithm for periodic-review perishable inventory systems. In contrast to the classical perishable inventory literature, we assume that the firm does not know the demand distribution a priori and makes replenishment decision in each period based only...

Prospect theory shows that human behavior in decision making can often be characterized by an S-shaped utility function. In this paper, we consider a periodic review inventory system with an exponential-type S-shaped utility function. The objective is to maximize the total expected utility over...

With the development of shared mobility (e.g., ride-sourcing systems such as Uber and Lyft), there has been a growing interest in pricing and empty vehicle relocation to maximize system performance (e.g., revenue, throughput, social welfare). Impatience of passengers during waiting is an...

In this paper we study a multi-period stochastic inventory system with backlogs. Demand in each period is random and price sensitive, but the firm has little or no prior knowledge about demand distribution and how each customer responds to the selling price, so the firm needs to make periodic...

When facing high levels of overstock inventories, firms often push their salesforce to work harder than usual to attract more demand, and one way to achieve that is to offer attractive incentives. However, most research on the optimal design of salesforce incentives ignores this dependency and...